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To find a numerical solution of the involved mathematical programming problem, we adopt an evolutionary algorithm based on the particle swarm optimization (PSO), which is an iterative metaheuristics grounded on swarm intelligence.
The pattern classifier system is a knowledge base made up of QFTRs that were learned with an evolutionary algorithm based on the cooperative-competitive approach together with token competition.
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(Liang et al. 2002a) proposed an evolutionary algorithm based on evolutionary programming for cutting stock problems considering contiguity.
Liu and Liu [3] introduced an evolutionary algorithm based on schema-guiding to solve 0 1 KP.
In this paper we propose two new parameter control strategies for evolutionary algorithms based on the ideas of reinforcement learning.
We also propose a constructive evolutionary algorithm based on tree-like representations of the morphology that can intrinsically provide for a type of generative evolutionary approach.
The overall optimisation is solved by a hybrid evolutionary algorithm based on a genetic algorithm.
In this paper, a dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model is proposed (DEE-DMOEA).
Particle swarm optimization (PSO) is a powerful stochastic evolutionary algorithm based on swarm intelligence.
Numerical results indicate a competitive performance of the evolutionary algorithm based on hill-climbing operators.
We find that the evolutionary algorithm based on interactive experiential learning remains valid.
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