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Proceedings Papers
. isal2024, ALIFE 2024: Proceedings of the 2024 Artificial Life Conference15, (July 22–26, 2024) 10.1162/isal_a_00729
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Coevolutionary learning is a flexible paradigm with many applications, but its practice is hindered by various subtle pathologies. The Discovery of Objectives via Clustering (DOC) algorithm is a heuristic approach for learner selection that employs the maximization of expected utility (MEU) solution concept. DOC shows some potential for addressing certain issues; however, modification is found necessary to prevent the pathology of overspecialization. We propose QueMEU, a novel test generator and memory mechanism that uses a sampling policy over a queue to maintain diversity and provide an effective learning gradient. Incorporation of Que- MEU improves the performance of DOC on abstract numbers game problems as well as the challenging density classification task. QueMEU outperforms the standard test generator as well as a more established alternative that employs fitness sharing.
Proceedings Papers
. isal2024, ALIFE 2024: Proceedings of the 2024 Artificial Life Conference66, (July 22–26, 2024) 10.1162/isal_a_00796
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Co-evolution is a powerful problem-solving approach. However, fitness evaluation in co-evolutionary algorithms can be computationally expensive, as the quality of an individual in one population is defined by its interactions with many (or all) members of one or more other populations. To accelerate co-evolutionary systems, we introduce phylogenyinformed interaction estimation, which uses runtime phylogenetic analysis to estimate interaction outcomes between individuals based on how their relatives performed against each other. We test our interaction estimation method with three distinct co-evolutionary systems: two systems focused on measuring problem-solving success and one focused on measuring evolutionary open-endedness. We find that phylogeny-informed estimation can substantially reduce the computation required to solve problems, particularly at the beginning of long-term evolutionary runs. Additionally, we find that our estimation method initially jump-starts the evolution of neural complexity in our open-ended domain, but estimation-free systems eventually “catch-up” if given enough time. Further refinements to these phylogeny-informed interaction estimation methods offer a promising path to reducing the computational cost of running co-evolutionary systems while maintaining their open-endedness.
Proceedings Papers
. isal2023, ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference49, (July 24–28, 2023) 10.1162/isal_a_00647
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Much activity in the field of artificial life has been concerned with the search for appropriate and effective methods of quantifying open-ended behavior in evolutionary systems. The MODES Toolbox is a recent addition that employs a persistence filter over evolutionary lineages to focus attention only on those genotypes that are most adaptive. The Toolbox provides a useful and intuitive set of metrics in terms of change, novelty, complexity, and ecology. One domain thought to exhibit open-ended dynamics, the linguistic prediction game, is a ripe candidate for deeper statistical analysis. We apply the MODES Toolbox on this domain, lending support to prior hypotheses regarding evolutionary stable states while also suggesting that, in at least one case, the observed complexity growth is largely nonadaptive.
Proceedings Papers
. isal2022, ALIFE 2022: The 2022 Conference on Artificial Life9, (July 18–22, 2022) 10.1162/isal_a_00486
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We study the conditions in which the unbounded growth of complexity – measured in terms of expressed genome size – can be observed in coevolving populations of neural agents involved in different classes of interactions. To reproduce the results of prior work on the dynamics of open-ended evolution, we introduce a simple pursuit-evasion scenario that allows for the development of increasingly intricate strategies. It is shown that for some configurations of our game, fitness-proportionate selection leads to stagnation while more sophisticated coevolutionary methods produce apparently unbounded complexity growth. Analysis of behavioral patterns sheds some light on the evolutionary pressures introduced by the model. Our findings replicate many features of previously reported work; however, we observe particular dynamics that differ in important respects, challenging prior conclusions, creating new opportunities, and highlighting the need for further investigation of this domain.
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life39-46, (July 23–27, 2018) 10.1162/isal_a_00014
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We present a method for using neural networks to model evolutionary population dynamics, and draw parallels to recent deep learning advancements in which adversarially-trained neural networks engage in coevolutionary interactions. We conduct experiments which demonstrate that models from evolutionary game theory are capable of describing the behavior of these neural population systems.
Proceedings Papers
. ecal2017, ECAL 2017, the Fourteenth European Conference on Artificial Life298-205, (September 4–8, 2017) 10.1162/isal_a_051
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We propose a linguistic prediction game with competitive and cooperative variants, and a model of game players based on finite state automata. We present a complexity metric for these automata, and study the coevolutionary dynamics of complexity growth in a variety of multi-species simulations. We present quantitative results using this complexity metric and analyze the causes of varying rates of complexity growth across different types of interactions. We find that while both purely competitive and purely cooperative coevolution are able to drive complexity growth above the rate of genetic drift, mixed systems with both competitive and cooperative interactions achieve significantly higher evolved complexity.
Proceedings Papers
. ecal2017, ECAL 2017, the Fourteenth European Conference on Artificial Life267-274, (September 4–8, 2017) 10.1162/isal_a_047
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We examine hierarchical modularity - modularity on multiple levels, in which the modules at a lower level of abstraction can serve as nodes in a network at a higher level of abstraction that also has positive modularity - as well as degree of modularity on a single level of abstraction, in evolved neural networks in single-task, parallel-subtask environments, and sequential-subtask environments, using a common benchmark problem. We determine that top-performing networks evolved in the sequential-subtask environment have both more levels of hierarchical modularity, and a higher degree of modularity within levels, than those involved in either the single-task or parallel-subtask environment. In the single-task environment, both single-level and hierarchical modularity tend to rise initially before stagnating and even declining, while in the sequential-subtask environment, both single-level and hierarchical modularity tend to rise throughout the period of evolution.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems344-351, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch058
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While it has been observed (Hornby et al., 2001) that developmental encodings in evolved systems may promote modularity, there has been little quantitative study of this phenomenon. There has also been little study of the factors driving the emergence of hierarchical modularity - modularity on multiple levels, in which the modules found at a finer-grained level can serve as elements in a coarser-grained network that is also modular - despite the fact that most fields with an interest in modularity, including biology and engineering, define hierarchy as an important aspect of modularity. We examine the effect of developmental encodings on the emergence of multiple levels of modularity through the lens of two developmental systems, GRNEAT and GENRE, and find evidence that developmental encodings promote this emergence of modular hierarchy.
Proceedings Papers
. alife2014, ALIFE 14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems827-834, (July 30–August 2, 2014) 10.1162/978-0-262-32621-6-ch135
Proceedings Papers
. alife2014, ALIFE 14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems821-826, (July 30–August 2, 2014) 10.1162/978-0-262-32621-6-ch134
Proceedings Papers
. alife2012, ALIFE 2012: The Thirteenth International Conference on the Synthesis and Simulation of Living Systems491-498, (July 19–22, 2012) 10.1162/978-0-262-31050-5-ch064
Proceedings Papers
. ecal2011, ECAL 2011: The 11th European Conference on Artificial Life6, (August 8–12, 2011) 10.7551/978-0-262-29714-1-ch006