Evolution of obstacle avoidance behavior: using noise to promote robust solutions
Advances in genetic programming
Advances in genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Noisy Local Optimization with Evolution Strategies
Noisy Local Optimization with Evolution Strategies
Viral infection + tropism for improving small population performance under noisy environment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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In this paper we report on a study in which genetic algorithms are applied to the analysis of noisy time-series signals, which is related to the problem of analyzing the motion characteristics of moving bodies (distance, bearing, course, velocity, etc.) by covertly sampling the sound of moving objects with submarine monitoring systems that track moving objects travelling on or through the water. In particular, we propose improving the system's ability to search through noisy data by grafting viruses onto the chromosomes used in genetic algorithms. Specifically, we propose a search method that can cope robustly with noise through the cooperative action of a wide-area search implemented by host chromosomes and a local search implemented by viruses grafted onto these chromosomes. To improve the infection rate, we also impose limits on the types of host entity that can be infected by viruses. By conducting evaluation tests in computer simulations, we show that the proposed technique can achieve a better rate of convergence and is capable of searching for a solution with fewer entities.