Hybridizing the dynamic mutation approach with local searches to overcome local optima

Wei, Kuai; Dinneen, Michael J.

doi:10.1109/cec.2014.6900360

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Furthermore, the interaction of mutations and local searches has attracted much attention. For example, Dinneen and Wei [4], in 2013, analyzed a dynamic mutation with two different local searches on some artificially created functions; they also analyzed a (1+1) Adaptive MA on the clique problem and showed that, for any local optimum that is hard to escape, the (1+1) Adaptive MA is expected to overcome the local optima super-polynomially faster than the basic (1+1) EA [3]; and a further study in [18] showed that hybridizing the dynamic mutation approach with local searches greatly enhances the algorithm's ability to escape local optima. More details of runtime analysis on EAs and MAs can be found in surveys [1], [7], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Runtime analysis to compare best-improvement and first-improvement in memetic algorithms

Wei

Dinneen

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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In recent years, the advantage afforded by using multiple local searches in a Memetic Algorithm (MA) to solve one problem (a single fitness function), has been verified in many successful experiments. These experiments also give the observation that the local search operator that gives the best results in an MA on the same fitness function for solving a NP-hard problem is instance specific. This paper will provide a theoretical evidence for this observation. In this paper, we will formalize the (1+1) Restart Memetic Algorithms applying two different local searches, the first-improvement and the best-improvement, respectively. We will then run them on a single fitness function to solve the Clique Problem. We then show that there are two families of graphs such that, for the first family of graphs, MAs with one local search drastically outperform MAs with the other local search, and vice versa for the second family of graphs. Our study explains why using multiple local searches can outperform using a single local search in Memetic Algorithms.

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Section: Introductionmentioning

confidence: 99%

Runtime analysis to compare best-improvement and first-improvement in memetic algorithms

Wei

Dinneen

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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“…Some of the examples are: the study on dynamic mutation approach [6], [5], [11], [28]; the recombination (also known as crossover) operation [10], [12], [17]; and the analysis of population-based EAs, [9], [30]. Therefore, theoretical analyses of MAs have mainly focused on the impact of the local searches.…”

Section: Introductionmentioning

confidence: 99%

Runtime analysis comparison of two fitness functions on a memetic algorithm for the Clique Problem

Wei

Dinneen

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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It is commonly accepted that a proper fitness function can guide the algorithm to find a global optimum solution faster. This paper will use the runtime analysis to provide the theoretical evidence that a small change of the fitness function (additional one step looking forward) can result in a huge performance gap in terms of finding a global optimum solution. It also shows that the fitness function that gives the best results in an Memetic Algorithm on the Clique Problem is entirely instance specific. In detail, we will formalize a (1+1) Restart Memetic Algorithm with a Best-Improvement Local Search, and run them on two different fitness functions, fOL and fOPL, to solve the Clique Problem respectively. We then construct two families of graphs, G1 and G2, and show that, for the first family of graphs G1, the (1+1) RMA on the fitness function fOPL drastically outperforms the (1+1) RMA on the fitness function fOL, and vice versa for the second family of graphs G2.

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Runtime analysis of some hybrid algorithms

Lai

Zhou

2023

Neural Comput & Applic

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Hybridizing the dynamic mutation approach with local searches to overcome local optima

Cited by 3 publications

References 19 publications

Runtime analysis to compare best-improvement and first-improvement in memetic algorithms

Runtime analysis to compare best-improvement and first-improvement in memetic algorithms

Runtime analysis comparison of two fitness functions on a memetic algorithm for the Clique Problem

Runtime analysis of some hybrid algorithms

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