Population-Based Incremental Learning With Associative Memory for Dynamic Environments

• Published in: IEEE transactions on evolutionary computation Vol. 12; no. 5; pp. 542 - 561
• Main Authors: Shengxiang Yang, Shengxiang Yang, Xin Yao, Xin Yao
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-10-2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Application software; Applied sciences; Artificial intelligence; Associative memory; Associative memory scheme; Computer applications; Computer science; Computer science; control theory; systems; Councils; Design optimization; Dopants; dynamic optimization problems (DOPs); Dynamic tests; Dynamical systems; Dynamics; Evolutionary algorithms; Evolutionary computation; Exact sciences and technology; Genetic algorithms; immune system-based genetic algorithm (ISGA); Laboratories; Learning; memory-enhanced genetic algorithm; multipopulation scheme; population-based incremental learning (PBIL); random immigrants; Studies; System testing; Probabilistic approach; Evolutionary algorithm; Adaptability; immune system-based genetic algorithm (ISGA); memory-enhanced genetic algorithm; Immune algorithm; Associative memory; random immigrants; Associative memory scheme; Genetic algorithm; Dynamic programming; dynamic optimization problems (DOPs); population-based incremental learning (PBIL); Learning algorithm; Artificial intelligence; multipopulation scheme; Mathematical programming; Immune system
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2007.913070

In recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown due to its importance in real-world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPs. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multipopulation, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator, a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multipopulation schemes for PBILs in different dynamic environments.