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Resilient individuals improve evolutionary search.

Terence Soule¹

¹University of Idaho, Department of Computer Science, Moscow, ID 83844-1010, USA. tsoule@cs.uidaho.edu

Artificial Life

|January 6, 2006

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Increasing genetic robustness allows populations to shift to higher, narrower fitness peaks. Restricting this resiliency can trap populations on less fit, broader peaks, hindering evolutionary search.

Area of Science:

Evolutionary computation
Artificial life
Genetics

Background:

Evolving populations often favor broader, less fit peaks over narrower, more fit ones.
Genetic robustness (resiliency) is the ability of an individual to resist negative effects of genetic operations.

Purpose of the Study:

To demonstrate a novel evolutionary dynamic where increased resiliency leads to shifting fitness peaks.
To investigate the role of resiliency in accessing narrower, higher fitness peaks.

Main Methods:

Simulations of evolving populations on fitness landscapes.
Analysis of population dynamics in relation to genetic robustness and fitness peak selection.

Main Results:

Populations initially on broad, low fitness peaks shifted to narrower, higher peaks when resiliency increased.

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Restricting resiliency-enhancing strategies, like growth, prevented this shift, trapping populations on suboptimal peaks.

Increased resiliency is a necessary precursor for evolutionary search to find narrower, fitter peaks.

Conclusions:

Genetic robustness is crucial for evolutionary algorithms to escape local optima and find superior solutions.
Strategies that limit resiliency can significantly impede evolutionary search by preventing access to better, narrower fitness peaks.