On the evolution of motility and intelligent tactic response

Grabowski, Laura M.; Elsberry, Wesley R.; Ofria, Charles; Pennock, Robert T.

doi:10.1145/1389095.1389129

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…Digital evolution has a proven track record of expanding evolutionary theory (Wilke et al 2001;Lenski et al 2003;Chow et al 2004) with supporting evidence often collected later in biological systems (Codoñer et al 2006). Previous studies in Avida have also demonstrated the evolution of instinctive navigation, such as gradient ascent and trail-following behavior (Grabowski et al 2008), including the genetically encoded use of memory to dictate subsequent behavior (Grabowski et al 2010). Here, we extend this work beyond reflexive behaviors to study the evolution of associative learning where each individual organism must discover a mapping between environmental cues and the optimal response.…”

Section: Introductionmentioning

confidence: 90%

The Evolutionary Origin of Associative Learning

Pontes

Mobley

Ofria

et al. 2020

The American Naturalist

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Learning is a widespread ability among animals and, like physical traits, is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers to these questions. Here, we take a novel approach by studying digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a nonlearning sessile ancestor, we evolve multiple populations in four different environments, each consisting of nutrient trails with various layouts. Trail nutrients cue organisms on which direction to follow, provided they evolve to acquire and use those cues. Thus, each organism is tested on how well it navigates a randomly selected trail before reproducing. We find that behavior evolves modularly and in a predictable sequence, where simpler behaviors are necessary precursors for more complex ones. Associative learning is only one of many successful behaviors to evolve, and its origin depends on the environment possessing certain information patterns that organisms can exploit. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations but remain consistent for periods within an organism's lifetime foster the evolution of learning behavior. Both types of environmental patterns are necessary, since the prior evolution of simple reflexive behaviors provides the building blocks for learning to arise. Finally, we observe that an intrinsic value system evolves alongside behavior and supports associative learning by providing reinforcement for behavior conditioning.

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Section: Introductionmentioning

confidence: 90%

The Evolutionary Origin of Associative Learning

Pontes

Mobley

Ofria

et al. 2020

The American Naturalist

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“…Avidians are self-replicating, and offspring are typically placed in a random neighboring cell. Avidians are able to move about their environment [32]. Each organism is born facing a particular neighbor cell.…”

Section: The Avida Digital Evolution Platformmentioning

confidence: 99%

Evolving cooperative pheromone usage in digital organisms

Connelly

McKinley

Beckmann

2009

2009 IEEE Symposium on Artificial Life

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Abstract-The use of chemicals to communicate among organisms has enabled countless species, from microorganisms, to colonies of insects, to mammals, to survive and flourish in their respective environments. Ants, arguably nature's most successful exploiters of this behavior, have evolved the use of pheromones to communicate in a wide range of situations, including mating, colony recognition, territory marking, and recruitment to new nest sites and food sources. We examine the evolution of the use of pheromones to aid in the location of, and migration to, a target area by groups of digital organisms. In an initial set of experiments, these organisms evolved efficient patterns of exploration that obviated the need for pheromones. When evolved in a more adverse environment, organisms again evolved effective search strategies, but also evolved the use of pheromones to enable the task to be completed by group members more quickly and with fewer movements. We also show that evolved organisms are more robust and better able to react to a change in the environment than a handbuilt solution. This work demonstrates the complexities that exist in the evolution of pheromone-enabled cooperation and provides insight into the behaviors executed by seemingly simple organisms in nature.

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“…That is, in Avida, the behavior and replication of organisms is based on a digital computer instead of on GRNs. Despite such divergence, and with few domain-specific modifications, experiments in Avida have led to demonstrations of how complex features can evolve [61], how sleep can be adaptive [7], and how motility can evolve [40]. Furthermore, Avida has practical applications in software development [72], asynchronous sensor communication [72], and energy management [72].…”

Section: Avidamentioning

confidence: 99%

Investigating Biological Assumptions through Radical Reimplementation

Lehman

Stanley

2015

Artificial Life

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An important goal in both artificial life and biology is uncovering the most general principles underlying life, which might catalyze both our understanding of life and engineering life-like machines. While many such general principles have been hypothesized, conclusively testing them is difficult because life on Earth provides only a singular example from which to infer. To circumvent this limitation, this paper formalizes an approach called radical reimplementation. The idea is to investigate an abstract biological hypothesis by intentionally reimplementing its main principles to diverge maximally from existing natural examples. If the reimplementation successfully exhibits properties resembling biology it may better support the underlying hypothesis than an alternative example inspired more directly by nature. The approach thereby provides a principled alternative to a common tradition of defending and minimizing deviations from nature in artificial life. This work reviews examples that can be interpreted through the lens of radical reimplementation to yield potential insights into biology despite having purposefully unnatural experimental setups. In this way, radical reimplementation can help renew the relevance of computational systems for investigating biological theory and can act as a practical philosophical tool to help separate the fundamental features of terrestrial biology from the epiphenomenal.

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On the evolution of motility and intelligent tactic response

Cited by 7 publications

References 25 publications

The Evolutionary Origin of Associative Learning

The Evolutionary Origin of Associative Learning

Evolving cooperative pheromone usage in digital organisms

Investigating Biological Assumptions through Radical Reimplementation

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