Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II (videotape): the next generation
Genetic programming II (videotape): the next generation
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Finite Markov chain results in evolutionary computation: a tour d'horizon
Fundamenta Informaticae
Branching programs and binary decision diagrams: theory and applications
Branching programs and binary decision diagrams: theory and applications
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Inver-over Operator for the TSP
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Design of Graph-Based Evolutionary Algorithms: A Case Study for Chemical Process Networks
Evolutionary Computation
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Analysis of an asymmetric mutation operator
Evolutionary Computation
Analyzing different variants of immune inspired somatic contiguous hypermutations
Theoretical Computer Science
| Hi-index | 0.00 |
In this chapter a set of guidelines for the design of genetic operators and the representation of the phenotype space in evolutionary algorithms (EAs) is proposed. These guidelines should help to systematize the design of problem-specific EAs by making the genetic operators behave in a controlled fashion with respect to metrics on geno- and phenotype space. Because we assume that we have enough domain knowledge to choose metrics that smooth the fitness landscape, this controlled behavior should improve the efficiency of the EA.The applicability of this concept is shown by the systematic design of a genetic programming system for finding Boolean functions. This system is the first genetic programming system to have reportedly found the 12 parity function.