Title

Improving Evolvability Through Novelty Search And Self-Adaptation

Abstract

A challenge for current evolutionary algorithms is to yield highly evolvable representations like those in nature. Such evolvability in natural evolution is encouraged through selection: Lineages better at molding to new niches are less susceptible to extinction. Similar selection pressure is not generally present in evolutionary algorithms; however, the first hypothesis in this paper is that novelty search, a recent evolutionary technique, also selects for evolvability because it rewards lineages able to continually radiate new behaviors. Results in experiments in a maze-navigation domain in this paper support that novelty search finds more evolvable representations than regular fitness-based search. However, though novelty search outperforms fitness-based search in a second biped locomotion experiment, it proves no more evolvable than fitness-based search because delicately balanced behaviors are more fragile in that domain. The second hypothesis is that such fragility can be mitigated through self-adaption, whereby genomes influence their own reproduction. Further experiments in fragile domains with novelty search and self-adaption indeed demonstrate increased evolvability, while, interestingly, adding self-adaptation to fitness-based search decreases evolvability. Thus, selecting for novelty may often facilitate evolvability when representations are not overly fragile; furthermore, achieving the potential of self-adaptation may often critically depend upon the reward scheme driving evolution. © 2011 IEEE.

Publication Date

8-29-2011

Publication Title

2011 IEEE Congress of Evolutionary Computation, CEC 2011

Number of Pages

2693-2700

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CEC.2011.5949955

Socpus ID

80051992006 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/80051992006

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