An introduction to genetic algorithms
An introduction to genetic algorithms
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Coding TSP tours as permutations via an insertion heuristic
Proceedings of the 1999 ACM symposium on Applied computing
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Inver-over Operator for the TSP
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Minimizing Cycle Time of the Flow Line --- Genetic Approach with Gene Expression
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
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We describe an environment for evolutionary computation that supports the movement of information from genome to phenotype with the possibility of one or more intermediate transformations. Our notion of a phenotype is more than a simple alternate representation of the binary genome. The construction of a phenotype is sufficiently different from the genome as to require its generation by a procedure that we call a gene expression algorithm. We discuss various reasons why benefits should accrue when combining gene expression algorithms with conventional genetic algorithms and illustrate these ideas with an algorithm to generate approximate solutions to the Traveling Salesperson Problem. As in most genetic algorithms dealing with the TSP we run into the problem of an appropriate crossover operation for the strings that specify a permutation. To handle this issue we introduce a novel genome representation that admits a natural crossover operation and produces a permutation vector as an intermediate representation. The gene expression strategy offers an excellent opportunity for parallelization of the computation since the gene expression processing for each genome and the subsequent evaluation of the fitness function are computations that can be spread across many processors.