Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for the 0/1 Knapsack Problem
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Using Genetic Algorithms to Optimize ACS-TSP
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Ant Colony Optimization
Evolutionary computation: a unified approach
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Nature-Inspired Algorithms for Optimisation
Nature-Inspired Algorithms for Optimisation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using river formation dynamics to design heuristic algorithms
UC'07 Proceedings of the 6th international conference on Unconventional Computation
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We present a method to compare the suitability of evolutionary computation metaheuristics for solving different NP-complete problems. Instead of checking the performance of each metaheuristic for each problem by using a specific benchmark of that problem, polynomial reductions are used to transform instances of a problem into the other. In this way, we avoid assessing each metaheuristic in terms of incomparable benchmarks. Several pairs of NP-complete problems are compared. For each pair, the impact of the difficulty of the polynomial reduction on the capability of an evolutionary method (in particular, genetic algorithms) to achieve a similar performance for both problems is studied.