Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
How to solve it: modern heuristics
How to solve it: modern heuristics
Co-evolutionary search in asymmetric spaces
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On classes of functions for which No Free Lunch results hold
Information Processing Letters
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Expert Systems with Applications: An International Journal
On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm
Engineering Applications of Artificial Intelligence
Conditions that Obviate the No-Free-Lunch Theorems for Optimization
INFORMS Journal on Computing
Research of multi-population agent genetic algorithm for feature selection
Expert Systems with Applications: An International Journal
Feature subspace ensembles: a parallel classifier combination scheme using feature selection
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper systematically proposed a multi-population agent co-genetic algorithm with double chain-like agent structure (MPATCGA) to solve the problem of the low optimization precision and long optimization time of simple genetic algorithm in terms of two coding strategy. This algorithm adopted multi-population parallel searching mode, close chain-like agent structure, cycle chain-like agent structure, dynamic neighborhood competition, and improved crossover strategy to realize parallel optimization, and has the characteristics of high optimization precision and short optimization time. Besides, the size of each sub-population is adaptive. The characteristic is very competitive when dealing with imbalanced workload. In order to verify the optimization precision of this algorithm with binary coding, some popular benchmark test functions were used for comparing this algorithm and a popular agent genetic algorithm (MAGA). The experimental results show that MPATCGA has higher optimization precision and shorter optimization time than MAGA. Besides, in order to show the optimization performance of MPATCGA with real coding, the authors used it for feature selection problems as optimization algorithm and compared it with some other well-known GAs. The experimental results show that MPATCGA has higher optimization precision (feature selection precision). In order to show the performance of the adaptability of size of sub-populations, MPATCGA with sub-populations with same size and MPATCGA with sub-populations with different size are compared. The experimental results show that when the workload on different sub-populations becomes not same, the adaptability will adaptively change the size of different sub-population to obtain precision as high as possible.