Publications

Below, I’ve listed all of the publications that I’m involved in. You can click on the title of the publication to download a free preprint for personal use, and click on the journal name for a link to the official journal listing of the publication.

If you would like a preprint of one of my publications that is not provided here, please contact me and I will be happy to send the preprint to you.

You can find my Google Scholar page at the following URL: http://scholar.google.com/citations?user=LU96GEAAAAAJ


A System for Accessible Artificial Intelligence

Randal S. Olson, Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, John H. Holmes, and Jason H. Moore (2017)
GPTP 2017, to appear.
BibTeX

@misc{Olson2017GPTP,
author = {Olson, Randal S. and Sipper, Moshe and La Cava, William and Tartarone, Sharon and Vitale, Steven and Fu, Weixuan and Holmes, John H. and Moore, Jason H.},
title = {A System for Accessible Artificial Intelligence},
year = {2017},
howpublished = {arXiv e-print. https://arxiv.org/abs/1705.00594},
}

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.


PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore (2017)
BioData Mining, to appear.
BibTeX

@misc{Olson2017PMLB,
author = {Olson, Randal S. and La Cava, William and Orzechowski, Patryk and Urbanowicz, Ryan J. and Moore, Jason H.},
title = {PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison},
year = {2017},
howpublished = {arXiv e-print. https://arxiv.org/abs/1703.00512},
}

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.


Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming

Andrew Sohn, Randal S. Olson, and Jason H. Moore (2017)
GECCO 2017, to appear.
BibTeX

@misc{Sohn2017GECCO,
author = {Sohn, Andrew and Olson, Randal S. and Moore, Jason H.},
title = {Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming},
year = {2017},
howpublished = {arXiv e-print. https://arxiv.org/abs/1702.01780},
}

Machine learning has been gaining traction in recent years to meet the demand for tools that can efficiently analyze and make sense of the ever-growing databases of biomedical data in health care systems around the world. However, effectively using machine learning methods requires considerable domain expertise, which can be a barrier of entry for bioinformaticians new to computational data science methods. Therefore, off-the-shelf tools that make machine learning more accessible can prove invaluable for bioinformaticians. To this end, we have developed an open source pipeline optimization tool (TPOT-MDR) that uses genetic programming to automatically design machine learning pipelines for bioinformatics studies. In TPOT-MDR, we implement Multifactor Dimensionality Reduction (MDR) as a feature construction method for modeling higher-order feature interactions, and combine it with a new expert knowledge-guided feature selector for large biomedical data sets. We demonstrate TPOT-MDR’s capabilities using a combination of simulated and real world data sets from human genetics and find that TPOT-MDR significantly outperforms modern machine learning methods such as logistic regression and eXtreme Gradient Boosting (XGBoost). We further analyze the best pipeline discovered by TPOT-MDR for a real world problem and highlight TPOT-MDR’s ability to produce a high-accuracy solution that is also easily interpretable.


Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases

Jason H. Moore, Peter C. Andrews, Randal S. Olson, Sarah E. Carlson, Curt R. Larock, Mario J. Bulhoes, James P. O’Connor, Ellen M. Greytak and Steven L. Armentrout (2017)
BioData Mining 10: 19.
BibTeX

@article{MooreMDR2017,
author={Moore, Jason H.
and Andrews, Peter C.
and Olson, Randal S.
and Carlson, Sarah E.
and Larock, Curt R.
and Bulhoes, Mario J.
and O’Connor, James P.
and Greytak, Ellen M.
and Armentrout, Steven L.},
title={Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases},
journal={BioData Mining},
year={2017},
volume={10},
number={1},
pages={19},
issn={1756-0381},
doi={10.1186/s13040-017-0139-3},
url={http://dx.doi.org/10.1186/s13040-017-0139-3}
}

Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway.


TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning

Randal S. Olson and Jason H. Moore (2016)
Proceedings of the 2016 Workshop on Automatic Machine Learning, pp. 66-74.
BibTeX

@article{Olson2016JMLR,
title={TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning},
author={Olson, Randal S. and Moore, Jason H.},
journal = {Journal of Machine Learning Research},
pages = {66–74},
volume = {64},
year={2016}
}

As data science becomes more mainstream, there will be an ever-growing demand for data science tools that are more accessible, flexible, and scalable. In response to this demand, automated machine learning (autoML) researchers have begun building systems that automate the process of designing and optimizing machine learning pipelines. In this paper we present TPOT, an open source genetic programming-based autoML system that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification task. We benchmark TPOT on a series of 150 supervised classification tasks and find that it significantly outperforms a basic machine learning analysis in 22 of them, while experiencing minimal degradation in accuracy on 5 of the benchmarks—all without any domain knowledge nor human input. As such, GP-based autoML systems show considerable promise in the autoML domain.


Evolution of active categorical image classification via saccadic eye movement

Randal S. Olson, Jason H. Moore, and Christoph Adami (2016)
Proceedings of PPSN 2016, pp. 581-590.
BibTeX

@inproceedings{Olson2016PPSN,
title={Evolution of active categorical image classification via saccadic eye movement},
author={Olson, Randal S. and Moore, Jason H. and Adami, Christoph},
booktitle={International Conference on Parallel Problem Solving from Nature},
pages={581–590},
year={2016},
organization={Springer}
}

Pattern recognition and classification is a central concern for modern information processing systems. In particular, one key challenge to image and video classification has been that the computational cost of image processing scales linearly with the number of pixels in the image or video. Here we present an intelligent machine (the “active categorical classifier,” or ACC) that is inspired by the saccadic movements of the eye, and is capable of classifying images by selectively scanning only a portion of the image. We harness evolutionary computation to optimize the ACC on the MNIST hand-written digit classification task, and provide a proof-of-concept that the ACC works on noisy multi-class data. We further analyze the ACC and demonstrate its ability to classify images after viewing only a fraction of the pixels, and provide insight on future research paths to further improve upon the ACC presented here.


Pareto Inspired Multi-Objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning

Ryan J. Urbanowicz, Randal S. Olson and Jason H. Moore (2016)
Proceedings of PPSN 2016, pp. 514-524.
BibTeX

@inproceedings{Urbanowicz2016PPSN,
title={Pareto Inspired Multi-objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning},
author={Urbanowicz, Ryan J and Olson, Randal S and Moore, Jason H},
booktitle={International Conference on Parallel Problem Solving from Nature},
pages={514–524},
year={2016},
organization={Springer}
}

Learning classifier systems (LCSs) are rule-based evolutionary algorithms uniquely suited to classification and data mining in complex, multi-factorial, and heterogeneous problems. The fitness of individual LCS rules is commonly based on accuracy, but this metric alone is not ideal for assessing global rule ‘value’ in noisy problem domains and thus impedes effective knowledge extraction. Multi-objective fitness functions are promising but rely on prior knowledge of how to weigh objective importance (typically unavailable in real world problems). The Pareto-front concept offers a multi-objective strategy that is agnostic to objective importance. We propose a Pareto-inspired multi-objective rule fitness (PIMORF) for LCS, and combine it with a complimentary rule-compaction approach (SRC). We implemented these strategies in ExSTraCS, a successful supervised LCS and evaluated performance over an array of complex simulated noisy and clean problems (i.e. genetic and multiplexer) that each concurrently model pure interaction effects and heterogeneity. While evaluation over multiple performance metrics yielded mixed results, this work represents an important first step towards efficiently learning complex problem spaces without the advantage of prior problem knowledge. Overall the results suggest that PIMORF paired with SRC improved rule set interpretability, particularly with regard to heterogeneous patterns.


Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

Randal S. Olson and Jason H. Moore (2016)
Genetic Programming Theory and Practice 2016, to appear.
BibTeX

@misc{Olson2016GPTP,
author = {Olson, Randal S. and Moore, Jason H.},
title = {Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool},
year = {2016},
howpublished = {arXiv e-print. http://arxiv.org/abs/1607.08878},
}

As data science continues to grow in popularity, there will be an increasing need to make data science tools more scalable, flexible, and accessible. In particular, automated machine learning (AutoML) systems seek to automate the process of designing and optimizing machine learning pipelines. In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem. Further, we analyze a large database of pipelines that were previously used to solve various supervised classification problems and identify 100 short series of machine learning operations that appear the most frequently, which we call the building blocks of machine learning pipelines. We harness these building blocks to initialize TPOT with promising solutions, and find that this sensible initialization method significantly improves TPOT’s performance on one benchmark at no cost of significantly degrading performance on the others. Thus, sensible initialization with machine learning pipeline building blocks shows promise for GP-based AutoML systems, and should be further refined in future work.


Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore (2016)
Proceedings of GECCO 2016, pages 485-492. Best paper, Evolutionary Machine Learning track.
BibTeX

@inproceedings{OlsonGECCO2016,
author = {Olson, Randal S. and Bartley, Nathan and Urbanowicz, Ryan J. and Moore, Jason H.},
title = {Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference 2016},
series = {GECCO ’16},
year = {2016},
isbn = {978-1-4503-4206-3},
location = {Denver, Colorado, USA},
pages = {485–492},
numpages = {8},
url = {http://doi.acm.org/10.1145/2908812.2908918},
doi = {10.1145/2908812.2908918},
acmid = {2908918},
publisher = {ACM},
address = {New York, NY, USA},
}

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning—pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.


Exploring the coevolution of predator and prey morphology and behavior

Randal S. Olson, Arend Hintze, Fred C. Dyer, Jason H. Moore, and Christoph Adami (2016)
Proceedings of ALIFE 2016, pages 250-257.
BibTeX

@inproceedings{Olson2016ALIFE,
author = {Olson, Randal S. and Hintze, Arend and Dyer, Fred C. and Moore, Jason H. and Adami, Christoph},
title = {Exploring the coevolution of predator and prey morphology and behavior},
booktitle = {Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems},
year = {2016},
series = {ALIFE 2016},
location = {Cancun, Mexico},
pages = {250–257},
numpages = {8},
publisher = {MIT Press},
address = {Cambridge, MA, USA}
}

A common idiom in biology education states, “Eyes in the front, the animal hunts. Eyes on the side, the animal hides.” In this paper, we explore one possible explanation for why predators tend to have forward-facing, high-acuity visual systems. We do so using an agent-based computational model of evolution, where predators and prey interact and adapt their behavior and morphology to one another over successive generations of evolution. In this model, we observe a coevolutionary cycle between prey swarming behavior and the predator’s visual system, where the predator and prey continually adapt their visual system and behavior, respectively, over evolutionary time in reaction to one another due to the well-known “predator confusion effect.” Furthermore, we provide evidence that the predator visual system is what drives this coevolutionary cycle, and suggest that the cycle could be closed if the predator evolves a hybrid visual system capable of narrow, high-acuity vision for tracking prey as well as broad, coarse vision for prey discovery. Thus, the conflicting demands imposed on a predator’s visual system by the predator confusion effect could have led to the evolution of complex eyes in many predators.


Flies as Ship Captains? Digital Evolution Unravels Selective Pressures to Avoid Collision in Drosophila

Ali Tehrani-Saleh, Randal S. Olson, and Christoph Adami (2016)
Proceedings of the ALIFE 2016, pages 554-561.
BibTeX

@inproceedings{Tehrani2016ALIFE,
author = {Tehrani-Saleh, Ali and Olson, Randal S. and Adami, Christoph},
title = {Flies as Ship Captains? Digital Evolution Unravels Selective Pressures to Avoid Collision in Drosophila},
booktitle = {Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems},
year = {2016},
series = {ALIFE 2016},
location = {Cancun, Mexico},
pages = {554–561},
numpages = {8},
publisher = {MIT Press},
address = {Cambridge, MA, USA}
}

Flies that walk in a covered planar arena on straight paths avoid colliding with each other, but which of the two flies stops is not random. High-throughput video observations, coupled with dedicated experiments with controlled robot flies have revealed that flies utilize the type of optic flow on their retina as a determinant of who should stop, a strategy also used by ship captains to determine which of two ships on a collision course should throw engines in reverse. We use digital evolution to test whether this strategy evolves when collision avoidance is the sole selective pressure. We find that the strategy does indeed evolve in a narrow range of cost/benefit ratios, for experiments in which the “regressive motion” cue is error free. We speculate that these stringent conditions may not be sufficient to evolve the strategy in real flies, pointing perhaps to auxiliary costs and benefits not modeled in our study.


Automating biomedical data science through tree-based pipeline optimization

Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore (2016)
Applications of Evolutionary Computation, pages 123-137. Best paper, EvoBio track.
BibTeX

@inbook{Olson2016EvoBio,
author={Olson, Randal S. and Urbanowicz, Ryan J. and Andrews, Peter C. and Lavender, Nicole A. and Kidd, La Creis and Moore, Jason H.},
editor={Squillero, Giovanni and Burelli, Paolo},
chapter={Automating Biomedical Data Science Through Tree-Based Pipeline Optimization},
title={Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part I},
year={2016},
publisher={Springer International Publishing},
pages={123–137},
isbn={978-3-319-31204-0},
doi={10.1007/978-3-319-31204-0_9},
url={http://dx.doi.org/10.1007/978-3-319-31204-0_9}
}

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning — pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators — such as synthetic feature constructors — that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.


Orthogonally Evolved AI to Improve Difficulty Adjustment in Video Games

Arend Hintze, Randal S. Olson, and Joel Lehman (2016)
Applications of Evolutionary Computation, pages 525-540. Best paper, EvoGames track.
BibTeX

@inbook{Hintze2016EvoGames,
author={Hintze, Arend and Olson, Randal S. and Lehman, Joel},
editor={Squillero, Giovanni and Burelli, Paolo},
chapter={Orthogonally Evolved AI to Improve Difficulty Adjustment in Video Games},
title={Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part I},
year={2016},
publisher={Springer International Publishing},
pages={525–540},
isbn={978-3-319-31204-0},
doi={10.1007/978-3-319-31204-0_34},
url={http://dx.doi.org/10.1007/978-3-319-31204-0_34}
}

Computer games are most engaging when their difficulty is well matched to the human player’s ability, thereby providing an appropriate challenge where the player is neither overwhelmed nor bored. In games where the player interacts with computer-controlled opponents, the difficulty of the game can be adjusted not only by changing the number of opponents or beneficial player resources in the game environment, but also through modifying the skill of the opponents. Applying evolutionary algorithms to evolve the artificial intelligence that controls opponent agents is one established method for adjusting opponent difficulty. Less-evolved agents (i.e. agents subject to fewer generations of evolution) provides easier opponents, while highly-evolved agents are more challenging to overcome. In this publication we test a new approach for difficulty adjustment in games: orthogonally evolved AI, where the player receives support from collaborating agents that are co-evolved with opponent agents (where collaborators and opponents have orthogonal incentives). The advantage is that game difficulty can be adjusted more granularly by manipulating two independent axes: by having more or less adept collaborators, and by having more or less adept opponents. Furthermore, human interaction can modulate (and be informed by) the performance and behavior of collaborating agents. In this way, orthogonally evolved AI both facilitates smoother difficulty adjustment and enables new game experiences.


Evolution of swarming behavior is shaped by how predators attack

Randal S. Olson, David B. Knoester, and Christoph Adami (2016)
Artificial Life 22, pages 299-318.
BibTeX

@article{Olson2016SelfishHerd,
author = {Olson, Randal S and Knoester, David B and Adami, Christoph},
title = {{Evolution of Swarming Behavior Is Shaped by How Predators Attack}},
journal = {Artificial Life},
year = {2016},
pages = {299–318},
volume = {22},
number = {3},
doi = {10.1162/ARTL_a_00206},
url = {http://dx.doi.org/10.1162/ARTL_a_00206},
month = aug
}

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton’s original formulation of domains of danger. Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton’s selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.


Exploring the evolution of a trade-off between vigilance and foraging in group-living organisms

Randal S. Olson, Patrick B. Haley, Fred C. Dyer, and Christoph Adami (2015)
Royal Society Open Science 2.
BibTeX

@article{Olson2015ManyEyes,
author = {Olson, Randal S. and Haley, Patrick B. and Dyer, Fred C. and Adami, Christoph},
title = {Exploring the evolution of a trade-off between vigilance and foraging in group-living organisms},
volume = {2},
number = {9},
year = {2015},
doi = {10.1098/rsos.150135},
publisher = {The Royal Society},
journal = {Royal Society Open Science}
}

Even though grouping behaviour has been actively studied for over a century, the relative importance of the numerous proposed fitness benefits of grouping remain unclear. We use a digital model of evolving prey under simulated predation to directly explore the evolution of gregarious foraging behaviour according to one such benefit, the “many eyes” hypothesis. According to this hypothesis, collective vigilance allows prey in large groups to detect predators more efficiently by making alarm signals or behavioural cues to each other, thereby allowing individuals within the group to spend more time foraging. Here, we find that collective vigilance is sufficient to select for gregarious foraging behaviour as long there is not a direct cost for grouping (e.g. competition for limited food resources), even when controlling for confounding factors such as the dilution effect. Furthermore, we explore the role of the genetic relatedness and reproductive strategy of the prey and find that highly related groups of prey with a semelparous reproductive strategy are the most likely to evolve gregarious foraging behaviour mediated by the benefit of vigilance. These findings, combined with earlier studies with evolving digital organisms, further sharpen our understanding of the factors favouring grouping behaviour.


Elucidating the evolutionary origins of collective animal behavior

Randal S. Olson (2015)
PhD thesis, Michigan State University.
BibTeX

@phdthesis{Olson2015Thesis,
title = {Elucidating the evolutionary origins of collective animal behavior},
school = {Michigan State University},
author = {Olson, Randal S.},
year = {2015},
month = {8}
}

Despite over a century of research, the evolutionary origins of collective animal behavior remain unclear. Dozens of hypotheses explaining the evolution of collective behavior have risen and fallen in the past century, but until recently it has been difficult to perform controlled behavioral evolution experiments to isolate these various hypotheses and test their individual effects. In this dissertation, I outline a relatively new method using digital models of evolution to perform controlled behavioral evolution experiments. In particular, I use these models to directly explore the evolutionary consequence of the selfish herd, predator confusion, and the many eyes hypotheses, and demonstrate how the models can lend key insights useful to behavioral biologists, computer scientists, and robotics researchers. This dissertation lays the groundwork for the experimental study of the hypotheses surrounding the evolution of collective animal behavior, and establishes a path for future experiments to explore and disentangle how the various hypothesized benefits of collective behavior interact over evolutionary time.


Navigating the massive world of reddit: using backbone networks to map user interests in social media

Randal S. Olson and Zachary P. Neal (2015)
PeerJ Computer Science 1, page e4.
BibTeX

@article{Olson2015RedditMap,
author = {Olson, Randal S. and Neal, Zachary P.},
doi = {10.7717/peerj-cs.4},
journal = {PeerJ Computer Science},
month = may,
title = {{Navigating the massive world of reddit: using backbone networks to map user interests in social media}},
url = {https://peerj.com/articles/cs-4/},
volume = {1},
pages = {e4},
year = {2015}
}

In the massive online worlds of social media, users frequently rely on organizing themselves around specific topics of interest to find and engage with like-minded people. However, navigating these massive worlds and finding topics of specific interest often proves difficult because the worlds are mostly organized haphazardly, leaving users to find relevant interests by word of mouth or using a basic search feature. Here, we report on a method using the backbone of a network to create a map of the primary topics of interest in any social network. To demonstrate the method, we build an interest map for the social news web site reddit and show how such a map could be used to navigate a social media world. Moreover, we analyze the network properties of the reddit social network and find that it has a scale-free, small-world, and modular community structure, much like other online social networks such as Facebook and Twitter. We suggest that the integration of interest maps into popular social media platforms will assist users in organizing themselves into more specific interest groups, which will help alleviate the overcrowding effect often observed in large online communities.


Risk sensitivity as an evolutionary adaptation

Arend Hintze, Randal S. Olson, Christoph Adami, and Ralph Hertwig (2015)
Scientific Reports 5.
BibTeX

@article{Hintze2015RiskSensitivity,
author = {Hintze, Arend and Olson, Randal S. and Adami, Christoph and Hertwig, Ralph},
doi = {10.1038/srep08242},
journal = {Scientific Reports},
month = feb,
title = {Risk sensitivity as an evolutionary adaptation},
url = {http://www.nature.com/srep/2015/150204/srep08242/full/srep08242.html},
volume = {5},
year = {2015}
}

Risk aversion is a common behavior universal to humans and animals alike. Economists have traditionally defined risk preferences by the curvature of the utility function. Psychologists and behavioral economists also make use of concepts such as loss aversion and probability weighting to model risk aversion. Neurophysiological evidence suggests that loss aversion has its origins in relatively ancient neural circuitries (e.g., ventral striatum). Could there thus be an evolutionary origin to risk aversion? We study this question by evolving strategies that adapt to play the equivalent mean payoff gamble. We hypothesize that risk aversion in this gamble is beneficial as an adaptation to living in small groups, and find that a preference for risk averse strategies only evolves in small populations of less than 1,000 individuals, or in populations segmented into groups of 150 individuals or fewer – numbers thought to be comparable to what humans encountered in the past. We observe that risk aversion only evolves when the gamble is a rare event that has a large impact on the individual’s fitness. As such, we suggest that rare, high-risk, high-payoff events such as mating and mate competition could have driven the evolution of risk averse behavior in humans living in small groups.


Evolving an optimal group size in groups of prey under predation

Patrick B. Haley, Randal S. Olson, Fred C. Dyer, and Christoph Adami (2015)
Proceedings of ECAL 2015, page 620.
BibTeX

@inproceedings{Haley2015ECAL,
author = {Haley, Patrick B. and Olson, Randal S. and Dyer, Fred C. and Adami, Christoph},
title = {Evolving an optimal group size in groups of prey under predation}
booktitle = {Proceedings of the Fourteenth European Conference on the Synthesis and Simulation of Living Systems},
series = {ECAL 2015},
year = {2015},
location = {York, United Kingdom},
pages = {620},
numpages = {1},
url = {http://dx.doi.org/10.7551/978-0-262-33027-5-ch107},
doi = {10.7551/978-0-262-33027-5-ch107},
publisher = {MIT Press},
address = {Cambridge, MA, USA}
}

Considerable progress has been made in understanding the evolutionary forces underlying animal group-living behavior. Even so, Krause and Ruxton (2002) identified optimal group size as an under-researched area characterized by unwieldy large-predator study systems and simulations based on group rather than individual decision-making. Therefore, we present a simple, flexible simulation of foraging and predation that demonstrates that the evolution of an optimal, evolutionarily stable group size is in fact possible.


Exploring conditions that select for the evolution of cooperative group foraging

Patrick B. Haley, Randal S. Olson, Fred C. Dyer, and Christoph Adami (2014)
Proceedings of ALIFE 2014, pages 310-311.
BibTeX

@inproceedings{Haley2014ALIFE,
author = {Haley, Patrick B. and Olson, Randal S. and Dyer, Fred C. and Adami, Christoph},
title = {Exploring conditions that select for the evolution of cooperative group foraging}
booktitle = {Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems},
series = {ALIFE 2014},
year = {2014},
location = {New York, New York, USA},
pages = {310–311},
numpages = {2},
url = {http://dx.doi.org/10.7551/978-0-262-32621-6-ch050},
doi = {10.7551/978-0-262-32621-6-ch050},
publisher = {MIT Press},
address = {Cambridge, MA, USA}
}

Many prey choose to live, forage, and reproduce in groups –this is one of the most readily-observed phenomena in biology. Group living is potentially costly (because of competitive interactions among other reasons), and the benefits that outweigh these costs are difficult to understand, as they may interact in complicated ways. Collective vigilance is one oft-cited benefit of grouping behaviors. This claim relies on the principle that at each moment in time prey must make a choice between two mutually exclusive actions: foraging for food or being vigilant to look for predators. Group foraging potentially allows individuals to increase their foraging efficiency–and therefore their fitness–by sharing the expensive task of looking out for predators. Since isolating such decision-making in biological systems is difficult (particularly on an evolutionary timescale), we use digital organisms to study how this decision is made by groups of prey under the threat of predation.


Predator confusion is sufficient to evolve swarming behavior

Randal S. Olson, Arend Hintze, Fred C. Dyer, David B. Knoester, and Christoph Adami (2013)
Journal of the Royal Society Interface 10: 20130305.
BibTeX

@article{Olson2013PredatorConfusion,
author = {Olson, Randal S. and Hintze, Arend and Dyer, Fred C. and Knoester, David B. and Adami, Christoph},
doi = {10.1098/rsif.2013.0305},
journal = {Journal of The Royal Society Interface},
month = aug,
number = {85},
pages = {20130305},
title = {Predator confusion is sufficient to evolve swarming behaviour},
url = {http://dx.doi.org/10.1098/rsif.2013.0305},
volume = {10},
year = {2013}
}

Swarming behaviors in animals have been extensively studied due to their implications for the evolution of cooperation, social cognition, and predator-prey dynamics. An important goal of these studies is discerning which evolutionary pressures favor the formation of swarms. One hypothesis is that swarms arise because the presence of multiple moving prey in swarms causes confusion for attacking predators, but it remains unclear how important this selective force is. Using an evolutionary model of a predator-prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behavior in prey. Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert pressure on the structure of the predator’s visual field, favoring the frontally oriented, high-resolution visual systems commonly observed in predators that feed on swarming animals. Finally, we provide evidence that when prey evolve swarming in response to predator confusion, there is a change in the shape of the functional response curve describing the predator’s consumption rate as prey density increases. Thus, we show that a relatively simple perceptual constraint–predator confusion–could have pervasive evolutionary effects on prey behavior, predator sensory mechanisms, and the ecological interactions between predators and prey.


Evolved digital ecosystems: Dynamic steady state, not optimal fixed point

Randal S. Olson, Masoud Mirmomeni, Tim Brom, Eric Bruger, Arend Hintze, David B. Knoester, and Christoph Adami (2013)
Proceedings of ECAL 2013, pages 126-133.
BibTeX

@inproceedings{Olson2013ECAL,
author = {Olson, Randal S. and Mirmomeni, Masoud and Brom, Tim and Bruger, Eric and Hintze, Arend and Knoester, David B. and Adami, Christoph},
title = {Evolved digital ecosystems: Dynamic steady state, not optimal fixed point},
booktitle = {Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems},
series = {ECAL 2013},
year = {2013},
location = {Taormina, Italy},
pages = {126–133},
numpages = {8},
url = {http://dx.doi.org/10.7551/978-0-262-31709-2-ch019},
doi = {10.7551/978-0-262-31709-2-ch019},
publisher = {MIT Press},
address = {Cambridge, MA, USA}
}

Traditional models of ecosystems often assume that the species composing an unperturbed ecosystem become fixed so that only the relative abundances of the species change over time. Such ecosystems are said to have reached an optimal fixed point. However, recent work has suggested that neutral evolutionary processes can significantly alter the species composition of an ecosystem, allowing the ecosystem to exist in a dynamic steady state. Here, we investigate the stability of ecosystems and the nature of the equilibrium that forms using the digital evolution platform Avida, tracking evolving ecosystems over thousands of generations. We find that the communities that form are remarkably stable, and do not experience a significant loss of diversity in the long run even in experimental treatments where the communities suffer catastrophic population bottlenecks. When diversity rebounds, ecological communities are reconstituted in a different form than the one that was destroyed, but this difference is comparable to the difference the system would have accumulated if it had been left untouched. Thus, digital ecological communities exist in a dynamic steady state, which ultimately eliminates the effect of historical disturbances.


Critical Interplay Between Density-dependent Predation and Evolution of the Selfish Herd

Randal S. Olson, David B. Knoester, and Christoph Adami (2013)
Proceedings of GECCO 2013, pages 247-254. Best paper, Artificial Life track.
BibTeX

@inproceedings{Olson2013SelfishHerd,
author = {Olson, Randal S. and Knoester, David B. and Adami, Christoph},
title = {Critical interplay between density-dependent predation and evolution of the selfish herd},
booktitle = {Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference},
series = {GECCO ’13},
year = {2013},
location = {Amsterdam, The Netherlands},
pages = {247–254},
numpages = {8},
url = {http://doi.acm.org/10.1145/2463372.2463394},
doi = {10.1145/2463372.2463394},
acmid = {2463394},
publisher = {ACM},
address = {New York, NY, USA}
}

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that the predator attack mode plays a critical role in the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton’s original formulation of “domains of danger.” Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work verifies Hamilton’s selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.


A Bottom-Up Approach to the Evolution of Swarming

Randal S. Olson, Christoph Adami, Fred C. Dyer, and Arend Hintze (2012)
Proceedings of ALIFE 2012, pages 567-568.
BibTeX

@inproceedings{Olson2012ALIFE,
author = {Olson, Randal S. and Adami, Christoph and Dyer, Fred C. and Hintze, Arend},
title = {A Bottom-Up Approach to the Evolution of Swarming},
booktitle = {Proceedings of the Thirteenth International Conference on the Synthesis and Simulation of Living Systems},
series = {ALIFE 2012},
year = {2012},
location = {East Lansing, Michigan, USA},
pages = {567–568},
numpages = {2},
publisher = {MIT Press},
address = {Cambridge, MA, USA}
}

One of nature’s most evident examples of self-organization is the formation of swarms, schools, or flocks of animals. These groups of individuals coordinate their movement on an individual basis to form self-organized collectives. It has been hypothesized that these aggregations of individuals improve mating success, or may be an adapted defense against predators by confusing the potential predator. In the past, these ostensibly complex swarming behaviors have been explained by the swarm members adhering to three simple rules: 1) Move in the same direction as your neighbors; 2) Remain close to your neighbors; and 3) Avoid collisions with your neighbors. We characterize this model as a top-down approach, where the behavior of the group is explained by simple rules that were conceived ad hoc and work only when applied to that particular system. Generally, this approach requires knowledge about the position and motion vector of nearby agents and therefore requires complex mathematical computations to determine the motion of each agent in the swarm. We find it implausible that biological creatures in swarms are performing complex computations, such as determining the relative position and motion vector of nearby conspecifics, every millisecond to make a decision about where to move next. We suggest instead that there must be a simpler, more computationally tractable mechanism (for biological organisms) that is guiding swarming behavior in nature. In this abstract we present a bottom-up approach, where each agent in the swarm is controlled individually by a Markov network brain as opposed to genetic programming or neural networks. The information provided to each swarm agent is limited to the information that the agent’s retina conveys, and every agent’s actions depend only on a combination of the swarm agent’s current sensory input (e.g., eyes and ears) and the state of internal nodes in the swarm agent’s Markov brain (i.e., memory). We suggest that this is a more realistic model of swarms observed in nature, since the information provided to the brain is simple to compute and decisions are made on an individual basis rather than by a top-down controller. This evolutionary agent-centered approach enables us to examine the environmental conditions that are conducive for swarming, and how these conditions influence the evolution of swarming behavior.


A step toward evolving biped walking behavior through indirect encoding

Randal S. Olson (2010)
Undergraduate Honors Thesis, University of Central Florida Burnett Honors College.

Teaching simulated biped robots to walk is a popular problem in machine learning. However, until this thesis, evolving a biped controller has not been attempted through an indirect encoding, i.e. a compressed representation of the solution, despite the fact that natural bipeds such as humans evolved through such an indirect encoding (i.e. DNA). Thus the promise for indirect encoding is to evolve gaits that rival those seen in nature. In this thesis, an indirect encoding called HyperNEAT evolves a controller for a biped robot in a computer simulation. To most effectively explore the deceptive behavior space of biped walkers, novelty search is applied as a fitness metric. The result is that although the indirect encoding can evolve a stable bipedal gait, the overall neural architecture is brittle to small mutations. This result suggests that some capabilities might be necessary to include beyond indirect encoding, such as lifetime adaptation. Thus this thesis provides fresh insight into the requisite ingredients for the eventual achievement of fluid bipedal walking through artificial evolution.