Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Finite Markov chain results in evolutionary computation: a tour d'horizon
Fundamenta Informaticae
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machines, Languages and Computation
Machines, Languages and Computation
Metric Based Evolutionary Algorithms
Proceedings of the European Conference on Genetic Programming
Design of Graph-Based Evolutionary Algorithms: A Case Study for Chemical Process Networks
Evolutionary Computation
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
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The optimization of structures is important for many industrial applications. But the problem of structure optimization is hardly understood. In the field of evolutionary computation mostly syntactical (pure structure-based) variation operators are used. For this kind of variation operators it is difficult to integrate domain-knowledge and to control the size of a mutation step. To gain insight into the basic problems of structure optimization we analyze mutation operators for evolutionary programming. For a synthetic problem we are able to derive a semantical mutation operator. The semantical mutation operator makes use of domain knowledge and has a well-defined parameter to adjust the step size.