Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Adaptive Evolutionary Algorithm for Numerical Optimization
SEAL'96 Selected papers from the First Asia-Pacific Conference on Simulated Evolution and Learning
Adaptive Polyclonal Programming Algorithm with Applications
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
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In this paper, Immune Clonal Selection theory and Lamarckism are integrated to form a new algorithm, Lamarckian Polyclonal Programming Algorithm (LPPA), for solving the global numerical optimization problem. The idea that Lamarckian evolution described how organism can evolve through learning, namely the point of “Gain and Convey” is applied, then this kind of learning mechanism is introduced into Adaptive Polyclonal Programming Algorithm (APPA). In the experiments, ten benchmark functions are used to test the performance of LPPA, and the scalability of LPPA along the problem dimension is studied with great care. The results show that LPPA achieves a good performance when the dimensions are increased from 20-10,000. Moreover, even when the dimensions are increased to as high as 10,000, LPPA still can find high quality solutions at a low computation cost. Therefore, LPPA has good scalability and is a competent algorithm for solving high dimensional optimization problems.