Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An evolutionary approach to combinatorial optimization problems
CSC '94 Proceedings of the 22nd annual ACM computer science conference on Scaling up : meeting the challenge of complexity in real-world computing applications: meeting the challenge of complexity in real-world computing applications
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Parallel Genetic Simulated Annealing: A Massively Parallel SIMD Algorithm
IEEE Transactions on Parallel and Distributed Systems
Scheduling Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Applying Evolutionary Algorithms to Combinatorial Optimization Problems
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Fine-Grained Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Efficient parallel LAN/WAN algorithms for optimization: the MALLBA project
Parallel Computing
Distributed differential evolution with explorative---exploitative population families
Genetic Programming and Evolvable Machines
Identifying human miRNA targets with a genetic algorithm
ISB '10 Proceedings of the International Symposium on Biocomputing
Efficient parallel LAN/WAN algorithms for optimization. The mallba project
Parallel Computing
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This chapter compares the performance of six evolutionary algorithms, three sequential and three parallel, for solving combinatorial optimization problems. In particular, a generational, a steady-state, a cellular genetic algorithm, and their distributed versions were applied to the maximum cut problem, the error correcting code design problem, and the minimum tardy task problem. The algorithms were tested on a total of seven problem instances. The results obtained in this chapter are better than the ones previously reported in the literature in all cases except for one problem instance. The high quality results were achieved although no problem. specific changes of the evolutionary algorithms were made other than in the fitness function. Just the intrinsic search features of each class of algorithms proved to be powerful enough to solve a given problem instance. Some of the sequential, and almost every parallel algorithm, yielded fast and accurate results, although they sampled only a tiny fraction of the search space.