Towards a Generally Applicable Self-Adapting Hybridization of Evolutionary Algorithms

Jakob, Wilfried; Blume, Christian; Bretthauer, Georg

doi:10.1007/978-3-540-24854-5_81

Cited by 8 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Krasnogor [29], Ong and Keane [42], or our own results [22,23]. The second question -whether to update the chromosome or not -is discussed controversially in literature, see e.g.…”

Section: How Long Should the Local Search Be Run? This Results In Locmentioning

confidence: 70%

“…If it is, the offspring must be better than its parent to ensure the elitist nature of this acceptance rule. The diffusion model is the reason why we found Lamarckian evolution superior to Baldwinian [22,23] and therefore the chromosomes are updated in our AMmA.…”

Section: Evolutionary Algorithm Gleammentioning

confidence: 92%

“…[23]). However, Ong and Keane use it for meme selection only and the number of allowed evaluations per LS run is fixed to 100, a comparably low number for some of the nine local searchers involved.…”

Section: Related Workmentioning

confidence: 99%

“…For the SMA, the effect of improving all or just the best offspring was investigated earlier [23,24,25] and the results are summarised here. We use the term static all-improvement to distinguish it from its adaptive counterpart.…”

Section: Strategy Parametersmentioning

confidence: 99%

“…This makes them harder, so that there is more room for improvements by added local search, see [23]. The fractal function with its unknown optimum is really hard, while the Griewank function was added due to its epistatic properties.…”

Section: Test Casesmentioning

confidence: 99%

See 4 more Smart Citations

A general cost-benefit-based adaptation framework for multimeme algorithms

Jakob

2010

Memetic Comp.

Self Cite

View full text Add to dashboard Cite

As Memetic Algorithms (MA) are a crossbreed between local searchers and Evolutionary Algorithms (EA) spreading of computational resources between evolutionary and local search is a key issue for a good performance, if not for success at all. This paper summarises and continues previous work on a general cost-benefit-based adaptation scheme for the choice of local searchers (memes), the frequency of their usage, and their search depth. This scheme eliminates the MA strategy parameters controlling meme usage, but raises new ones for steering the adaptation itself. Their impact is analysed and it will be shown that in the end the number of strategy parameters is decreased significantly as well as their range of meaningful values. In addition to this the number of fitness evaluations is reduced drastically. Both are necessary prerequisites for many practical applications as well as for the acceptance of the method by practitioners.Although the introduced framework is tailored to EAs producing more than one offspring per mating, it is also suited for those with only one child per pairing. So there are no preconditions to the EA for the described adaptation scheme to be applied. The benefit of LS within an MA can be summarised as follows: firstly, the already mentioned reduction of fitness evaluations required to achieve a certain quality and secondly, the possibility to include domain-specific knowledge into the search process. Additionally, offspring of poor performance, which are located in a region of attraction of a (local) optimum, are likely to be ignored in standard EAs, but can survive, if locally improved and support subsequent search. Furthermore, local search can work as a repair mechanism, if the genetic operators produce infeasible solutions in constraint optimisation and these solutions are penalised by reducing their fitness. The drawback consists in the following design questions, the last two of which mainly control the balance between global and local search or between exploration and exploitation: Keywords1. Which local searcher should be used?2. Should the LS result be used to update the genotype (Lamarckian evolution)or not (Baldwinian evolution)?3. How are offspring selected to undergo local search?4. How often should local improvement be applied or to which fraction of the generated offspring per generation? This is often referred to as local search frequency. [14,55,44]. When introducing the basic algorithms used for the experimental study, our answer to this question will be given. 3In this paper we present a cost-benefit-based adaptation scheme, which controls the parameters resulting from the remaining four of the above five questions. The beneficial effect of the adaptation scheme was reported about in [24,25], where also the new strategy parameters resulting from the scheme were introduced and discussed. In this work, the adaptation scheme is extended and the effect of the new strategy parameters is investigated empirically in more detail. The main goal is to find out their rele...

show abstract

“…Krasnogor [29], Ong and Keane [42], or our own results [22,23]. The second question -whether to update the chromosome or not -is discussed controversially in literature, see e.g.…”

Section: How Long Should the Local Search Be Run? This Results In Locmentioning

confidence: 70%

Section: Evolutionary Algorithm Gleammentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Section: Strategy Parametersmentioning

confidence: 99%

Section: Test Casesmentioning

confidence: 99%

See 3 more Smart Citations

A general cost-benefit-based adaptation framework for multimeme algorithms

Jakob

2010

Memetic Comp.

Self Cite

View full text Add to dashboard Cite

show abstract

Towards an Adaptive Multimeme Algorithm for Parameter Optimisation Suiting the Engineers’ Needs

Jakob

2006

Parallel Problem Solving From Nature - PPSN IX

View full text Add to dashboard Cite

Towards a Generally Applicable Self-Adapting Hybridization of Evolutionary Algorithms

Jakob

Blume

Bretthauer

2004

Genetic and Evolutionary Computation – GECCO 2004

Self Cite

View full text Add to dashboard Cite

Abstract. When applied to real-world problems, the powerful optimization tool of Evolutionary Algorithms frequently turns out to be too time-consuming due to elaborate fitness calculations that are often based on run-time-intensive simulations. Incorporating domain-specific knowledge by problem-tailored heuristics or local searchers is a commonly used solution, but turns the generally applicable Evolutionary Algorithm into a problem-specific tool. The new method of hybridization implemented in HyGLEAM is aimed at overcoming this limitation and getting the best of both algorithm classes: A fast, globally searching, and robust procedure that preserves the convergence reliability of evolutionary search. Extensive tests demonstrate the superiority of the approach, but also show a drawback: No common parameterization can be drawn from the experiments. As a solution, a new concept of a self-adapting hybrid is introduced. It is stressed that the methods presented can be applied to Evolutionary Algorithms other than the one used here with no or minor modifications being required only.

show abstract

Towards a Generally Applicable Self-Adapting Hybridization of Evolutionary Algorithms

Cited by 8 publications

References 15 publications

A general cost-benefit-based adaptation framework for multimeme algorithms

A general cost-benefit-based adaptation framework for multimeme algorithms

Towards an Adaptive Multimeme Algorithm for Parameter Optimisation Suiting the Engineers’ Needs

Towards a Generally Applicable Self-Adapting Hybridization of Evolutionary Algorithms

Contact Info

Product

Resources

About