Abstract

AbstractEvolution is often considered a gradual hill climbing process, slowly increasing the fitness of organisms. I investigate the evolution of homing behaviour in simulated intertidal limpets. In order to evolve path integration as a homing mechanism, a temporary reduction in an organism’s fitness is required – since high developmental costs occur before a successful homing strategy evolves. Simple hill-climbing algorithms, therefore, only rarely result in the evolution of a functional homing behaviour. The inclusion of a second behaviour (trail-following) greatly increases the frequency of success of evolution of a path integration strategy. Initially an emergent homing behaviour is formed combining path integration with trail following. This also demonstrates evolution through exaptation, since the original role of trail following is likely to be unrelated to homing. Analysis of the fitness landscapes of homing in the presence of trail-following behaviour shows a high variability of fitness, which results in the formation of ‘stepping-stones’ of high fitness across fitness valleys. By using these stepping-stones, simple hill-climbing algorithms can reach the global maximum fitness value.

Highlights

  • IntroductionIn both real (biological) evolution and artificial evolution (evolutionary algorithms), a key concept is the gradual climbing of a ‘hill’, which represents fitness of the organism (Gould and Lewontin, 1979; Holland, 1992)

  • In both real evolution and artificial evolution, a key concept is the gradual climbing of a ‘hill’, which represents fitness of the organism (Gould and Lewontin, 1979; Holland, 1992)

  • This strategy can occur early in the evolution of homing, where values for the angle error (Angle) parameter and Trail parameter are low, but the Distance parameter is subject to large errors (Figure 2d; but note alternative example shown in Figure 3b, where the Distance parameter decreased first)

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Summary

Introduction

In both real (biological) evolution and artificial evolution (evolutionary algorithms), a key concept is the gradual climbing of a ‘hill’, which represents fitness of the organism (Gould and Lewontin, 1979; Holland, 1992). Small changes to the genotype, arising through mutation or a specific recombination of genes, result in some individuals being better adapted to their task than in the previous generation. These individuals will be more likely to reproduce or produce more offspring than individuals with no advantage, or with disadvantages over previous generations (reviewed by Gould, 2002). An accumulation of these small changes will result in genes that have evolved to be well adapted to their environment (Dawkins, 1996). Use a range of small hill-climbing mutations, along with larger mutations, immigration and complex recombination to locate these global optimum points (Mitchell and Forrest, 1998)

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