Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
A framework for evolutionary algorithms based on Charles Sanders Peirce's evolutionary semiotics
Information Sciences: an International Journal
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Historically, Evolutionary Algorithms (EAs) have been important for Evolutionary Computation (EC) community for two reasons: 1) As a simulation of evolutionary processes as they happen in nature, and 2) as a solution to hard optimization problems. With the passage of time EAs have become increasingly focused on function optimization. Given this narrowing of vision in the EC community, it is worth revisiting a paper written in 1997 by Hans-Paul Schwefel on the future challenges for EC. In that paper the author argues that the more an algorithm models natural evolution at work in the universe, the better it will perform (even in terms of function optimization). The present paper tests Schwefel's hypothesis by designing an EA based on Charles Peirce's theory of evolution. Peirce's theory not only accounts for biological evolution on earth (as other theories of evolution do) but also offers an account of global, cosmological and universal evolution. In going beyond just biological evolution, Peirce's theory of evolution meets the criteria suggested by Schewefel in his 1997 paper. The present paper mainly contributes in testing the Peircean EA on an extended set of benchmark optimization functions and compares the results with a classical EA that is based on Darwin's theory of evolution. In majority of these comparisons the performance of the Peircean EA is notably superior. This exercise provides preliminary results that support Schwefel's hypothesis. In return the experiments in evolutionary computation help provide important insights into Peirce's theory of evolution.