Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments

Abstract

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Direct Memory Schemes for Population-Based Incremental Learning in Cyclically Changing Environments

“…Since the direct memory scheme, i.e., memory-based immigrants, integrated to MIPBIL, was initially introduced and integrated to GAs [11], further pairwise comparisons are performed in this section. Specifically, MIPBIL is compared with memory-based immigrants GA (MIGA) [11].…”

“…Specifically, MIPBIL is compared with memory-based immigrants GA (MIGA) [11]. MIGA is executed on the same DOPs and common parameter settings with MIPBIL above (e.g., r i = 0.2, p i m = 0.05 and m = 0.1 × n).…”

“…Existing strategies, which have been integrated with PBILs and were mainly inspired by EAs, include associative memory schemes [3], hyper-learning schemes [4], multi-population schemes [7] and immigrants schemes [8,5]. In this paper, we integrate direct memory schemes, previously used to genetic algorithms (GAs) [11], to PBILs to address cyclically changing DOPs.…”

“…Direct memory schemes have showed promising performance in DOPs when applied to different GAs [11]. The difference of a direct memory scheme from the associative memory scheme described previously for the MPBIL is that the memory point consists of only the solution.…”

“…Memory-Based Immigrants PBIL Based on the above consideration, the memory-based immigrants scheme, which has been initially proposed for GAs [11] to address cyclic DOPs, can be integrated with PBILs, denoted as memorybased immigrants PBIL (MIPBIL) in this paper, to better tackle DOPs with reappearing environments. A direct memory scheme is maintained and updated as described previously for the MEPBIL and MRIPBIL algorithms.…”