On the Critical Role of Divergent Selection in Evolvability

Lehman, Joel; Wilder, Bryan; Stanley, Kenneth O.

doi:10.3389/frobt.2016.00045

Cited by 9 publications

(8 citation statements)

References 37 publications

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“…Finally, there is some evidence that the types of selection regimes typically used in experiments with changing environments and evolvability might preferentially favor individual evolvability (the probability of an individual’s offspring accessing novel phenotypes) over population-level evolvability (the probability of the population at large accessing novel phenotypes) [54, 55]. Adaptive selection—that is, selection toward a particular goal—has been shown to depress population diversity even while it increases individual evolvability in changing environment regimes.…”

Section: Introductionmentioning

confidence: 99%

Fluctuating environments select for short-term phenotypic variation leading to long-term exploration

2019

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Genetic spaces are often described in terms of fitness landscapes or genotype-to-phenotype maps, where each genetic sequence is associated with phenotypic properties and linked to other genotypes that are a single mutational step away. The positions close to a genotype make up its “mutational landscape” and, in aggregate, determine the short-term evolutionary potential of a population. Populations with wider ranges of phenotypes in their mutational neighborhood are known to be more evolvable. Likewise, those with fewer phenotypic changes available in their local neighborhoods are more mutationally robust. Here, we examine whether forces that change the distribution of phenotypes available by mutation profoundly alter subsequent evolutionary dynamics. We compare evolved populations of digital organisms that were subject to either static or cyclically-changing environments. For each of these, we examine diversity of the phenotypes that are produced through mutations in order to characterize the local genotype-phenotype map. We demonstrate that environmental change can push populations toward more evolvable mutational landscapes where many alternate phenotypes are available, though purely deleterious mutations remain suppressed. Further, we show that populations in environments with harsh changes switch phenotypes more readily than those in environments with more benign changes. We trace this effect to repeated population bottlenecks in the harsh environments, which result in shorter coalescence times and keep populations in regions of the mutational landscape where the phenotypic shifts in question are more likely to occur. Typically, static environments select solely for immediate optimization, at the expensive of long-term evolvability. In contrast, we show that with changing environments, short-term pressures to deal with immediate challenges can align with long-term pressures to explore a more productive portion of the mutational landscape.

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Section: Introductionmentioning

confidence: 99%

Fluctuating environments select for short-term phenotypic variation leading to long-term exploration

2019

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“…To reach a particular goal space more rapidly, a first possibility suggested by the proposed model is, of course, to increase the probability to generate a solution p A second possibility suggested by the model is to increase the reachability γ n,д . The reachability depends on the genotype [19,24], but also on the evolutionary process [12,13,26]. Likewise, the behavior function has a critical impact [7].…”

Section: Discussionmentioning

confidence: 99%

“…The reachability is individual-based and does not allow to make predictions if the corresponding genotypes are unknown, but reachability can also be seen at the population level [12,13,26]. To this end, we assume that reachability is a population-based property that depends on the number of individuals sampled so far, and not on the particular genotype x a new individual is generated from, i.e.…”

Section: Model Descriptionmentioning

confidence: 99%

Novelty search

Doncieux

Laflaquière²,

Coninx

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Novelty Search is an exploration algorithm driven by the novelty of a behavior. The same individual evaluated at different generations has different fitness values. The corresponding fitness landscape is thus constantly changing and if, at the scale of a single generation, the metaphor of a fitness landscape with peaks and valleys still holds, this is not the case anymore at the scale of the whole evolutionary process. How does this kind of algorithms behave? Is it possible to define a model that would help understand how it works? This understanding is critical to analyse existing Novelty Search variants and design new and potentially more efficient ones. We assert that Novelty Search asymptotically behaves like a uniform random search process in the behavior space. This is an interesting feature, as it is not possible to directly sample in this space: the algorithm has a direct access to the genotype space only, whose relationship to the behavior space is complex. We describe the model and check its consistency on a classical Novelty Search experiment. We also show that it sheds a new light on results of the literature and suggests future research work. CCS CONCEPTS• Computing methodologies → Search methodologies; Evolutionary robotics; Neural networks;

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“…Consequently, we estimate it on the basis of a large sampling of offspring: for each individual, a significant number of offspring is generated by applying the evolutionary process mutation operator. Those sets are then used to produce estimations of reachability and of uniformity, as detailed below, both per individual and at a population level [22]. For individual metrics, we analyze the set of generated offspring of each individual in the population individually.…”

Section: Evolvability: Definition and Estimationmentioning

confidence: 99%

“…Finding a selective pressure that would be simple and cheap to compute while indirectly fostering evolvability is thus of critical interest. Several properties or mechanisms have already been shown to increase evolvability: a pressure on neural network connection cost [3], fitness landscape ruggedness [16], extinction events [17], and divergent selection [22]. Among these different approaches, divergent selection is of particular interest as its general formalization imposes little constraints on the phenotype, problem dynamic or ruggedness.…”

Section: Introductionmentioning

confidence: 99%

Novelty search makes evolvability inevitable

Doncieux¹,

Paolo²,

Laflaquière

et al. 2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

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Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to solve. The estimation of evolvability is not straight-forward and is generally too expensive to be directly used as selective pressure in the evolutionary process. Indirectly promoting evolvability as a side effect of other easier and faster to compute selection pressures would thus be advantageous. In an unbounded behavior space, it has already been shown that evolvable individuals naturally appear and tend to be selected as they are more likely to invade empty behavior niches. Evolvability is thus a natural byproduct of the search in this context. However, practical agents and environments often impose limits on the reachable behavior space. How do these boundaries impact evolvability? In this context, can evolvability still be promoted without explicitly rewarding it? We show that Novelty Search implicitly creates a pressure for high evolvability even in bounded behavior spaces, and explore the reasons for such a behavior. More precisely we show that, throughout the search, the dynamic evaluation of novelty rewards individuals which are very mobile in the behavior space, which in turn promotes evolvability. CCS CONCEPTS• Computing methodologies → Search methodologies; Evolutionary robotics; Neural networks;

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On the Critical Role of Divergent Selection in Evolvability

Cited by 9 publications

References 37 publications

Fluctuating environments select for short-term phenotypic variation leading to long-term exploration

Fluctuating environments select for short-term phenotypic variation leading to long-term exploration

Novelty search

Novelty search makes evolvability inevitable

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