A comparison of dominance mechanisms and simple mutation on non-stationary problems

Lewis, John W.; Hart, Emma; Ritchie, Graéme

doi:10.1007/bfb0056857

Cited by 112 publications

(72 citation statements)

References 3 publications

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“…A straightforward method, but not efficient, to construct a dynamic test problem is to switch between different static instances that will cause an environmental change [64]. The benchmark problems that generate dynamic environments following this methodology are specified for a single problem.…”

Section: The Generation Of Dynamicsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: The Generation Of Dynamicsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

View full text Add to dashboard Cite

show abstract

“…Implicit memory schemes use redundant encodings, e.g., diploid genotype [10,12,18], to store information for EAs to exploit during the run. In contrast, explicit memory uses precise representations but splits an extra storage space to explicitly store information from a current generation and reuse it later [4,11,17].…”

Section: Memory Schemes For Dynamic Environmentsmentioning

confidence: 99%

Memory Based on Abstraction for Dynamic Fitness Functions

Richter

Yang

2008

Lecture Notes in Computer Science

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Abstract. This paper proposes a memory scheme based on abstraction for evolutionary algorithms to address dynamic optimization problems. In this memory scheme, the memory does not store good solutions as themselves but as their abstraction, i.e., their approximate location in the search space. When the environment changes, the stored abstraction information is extracted to generate new individuals into the population. Experiments are carried out to validate the abstraction based memory scheme. The results show the efficiency of the abstraction based memory scheme for evolutionary algorithms in dynamic environments.

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“…Therefore a mechanism to map the genotype onto the phenotype is needed. This is a very important part of diploid genetic algorithms and there has been some research done most of which are explained in detail in, [3], [4], [5], [6], [7], [8], [9], [10], [11], [12].…”

Section: Representation and The Domination Mechanismmentioning

confidence: 99%

“…However, as shown in [9] diploidy alone is not sufficient and other mechanisms combined with an adaptive dominance map is needed. This paper focuses on the importance of preserving diversity in the population and how different parts of the chosen diploid algorithm contributes to this end.…”

Section: Introductionmentioning

confidence: 99%

Preserving Diversity through Diploidy and Meiosis for Improved Genetic Algorithm Performance in Dynamic Environments

Etaner-Uyar

Harmanci

2002

Advances in Information Systems

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Abstract. Genetic algorithms have been applied to a diverse field of problems with promising results. Using genetic algorithms modified to various degrees for tackling dynamic problems has attracted much attention in recent years. The main reason classical genetic algorithms do not perform well in such problems is that they converge and lose their genetic diversity. However, to be able to adapt to a change in the environment, diversity must be maintained in the gene pool of the population. One approach to the problem involves a diploid representation of individuals. Using this representation with a dynamic dominance map mechanism and meiotic cell division helps preserve diversity. In this paper, the effects of using diploidy and meiosis with such a dominance mechanism are explored. Experiments are carried out using a variation of the 0-1 knapsack problem as a testbed to determine the effects of the different aspects of the approach on population diversity and performance. The results obtained show promising enhancements.

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A comparison of dominance mechanisms and simple mutation on non-stationary problems

Cited by 112 publications

References 3 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Memory Based on Abstraction for Dynamic Fitness Functions

Preserving Diversity through Diploidy and Meiosis for Improved Genetic Algorithm Performance in Dynamic Environments

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