An introduction to genetic algorithms
An introduction to genetic algorithms
Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
Creative evolutionary systems
SIAM Review
Fitness landscapes and evolvability
Evolutionary Computation
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Genome Growth and the Evolution of the Genotype-Phenotype Map
Evolution and Biocomputation, Computational Models of Evolution
Maximally rugged NK landscapes contain the highest peaks
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary Computation
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A system composed of multiple interacting components is capable of responding to contextual information and producing a higher range of non-linear responses to stimuli compared to modular systems with a low degree of component interaction. However, the fitness landscape of highly integrated systems is more rugged indicating that such systems are likely to be less evolvable. In this work we use an artificial life simulation to investigate whether the evolvability of highly integrated systems can be improved if thelevel of integration between the system's components is underevolutionary control. When evolving our multi-component system we discover that the level of integration very quickly falls to virtually zero reducing the ruggedness of the landscape and making it nearly neutral. This allows the evolving population to explore the genome space without getting stuck on local optima. The components then integrate and the evolving population settles on the global optimum. This work is unique because the presented problem requires the evolving system to be fully integrated in order to solve it and as such the decreased ruggedness and near neutrality are not a permanent feature of the landscape but rather a property which is temporarily manipulated and exploited by the evolving population.