From black-box complexity to designing new genetic algorithms

Doerr, Benjamin; Doerr, Carola; Ebel, Franziska

doi:10.1016/j.tcs.2014.11.028

Cited by 204 publications

(258 citation statements)

References 14 publications

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“…Other relevant parameters such as the mutation and crossover probabilities are required to be carefully selected to prevent jumping over the solutions they are close to and prevent getting stuck in local minima. Many research on optimal parameter settings in GA have been conducted 12 and presented the ways and benefits of choosing the optimal experimental parameters. As it was investigated by Alajmi and Wright 6 , selecting a small population size, high crossover probability, and low mutation rate are considered to be the most appropriate control parameters that could provide optimal solutions.…”

Section: Setting the Experimental Parametersmentioning

confidence: 99%

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Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Hassan¹,

Hamada²

2018

IJCIS

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We often make decisions on the things we like, dislike, or even don't care about. However, taking the right decisions becomes relatively difficult from a variety of items from different sources. Recommender systems are intelligent decision support software tools that help users to discover items that might be of interest to them. Various techniques and approaches have been applied to design and implement such systems to generate credible recommendations to users. A multi-criteria recommendation technique is an extended approach for modeling user's preferences based on several characteristics of the items. This research presents genetic algorithm-based approaches for predicting user preferences in multi-criteria recommendation problems. Three genetic algorithms' methods, namely standard genetic algorithm, adaptive genetic algorithm, and multi-heuristic genetic algorithms are used to conduct the experiments using a multi-criteria dataset for movies recommendation. The empirical results of the comparative analysis of their performance are presented in this study.

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Section: Setting the Experimental Parametersmentioning

confidence: 99%

“…Pre-computation step computes the value of Δ i, j using (11), where N i, j is the number of users who rated i and j. Finally, the unknown rating of item k by the user u is predicted from (12).…”

Section: Overall Rating Predictionmentioning

confidence: 99%

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Hassan¹,

Hamada²

2018

IJCIS

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“…It can also be applied beyond mutation-based algorithms to genetic algorithms with crossover if the restriction of a population of size only 1 is met. An example for such an algorithm is the so-called (1+(λ, λ)) GA [7], the first realistic evolutionary algorithm to provably beat the Ω (n log n) lower bound on OneMax. We see that the framework is much more general and useful than it may appear at first sight.…”

Section: Lemma 3 If the Expected Time T (A F X) Is Used As The Drifmentioning

confidence: 99%

“…From the table it can be noticed that both levels L 7 and L 8 contain bit strings with two 1-blocks and four 1-bits. However, the lengths of the two 1-blocks are different (i. e., three and one for L 7 and two and two for L 8 ). The reason why levels L 7 and L 8 are distinct is highlighted in the last two columns of Table 1, which report the probabilities of reaching levels of higher fitness, respectively from levels L 7 and L 8 .…”

Section: On the Easiest Function For Hybrid Algorithmsmentioning

confidence: 99%

“…Firstly, in Sect. 3 it is discussed how the framework for determining easiest and hardest functions is not restricted to (1+1)-style algorithms, but is general enough to apply for a much larger class of algorithms including (1+λ) EAs [13] and the recently popular (1+(λ, λ)) GA using mutation and crossover [7]. Secondly, the analysis of the advantages of hybridisation in Sect.…”

Section: Introductionmentioning

confidence: 99%

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On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation

Corus

Jansen

et al. 2016

Algorithmica

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Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1) EA using standard bit mutation (SBM) it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest. In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1) EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We rigorously prove that by combining the advantages of k operators, several hybrid algorithmic schemes have optimal asymptotic performance on the easiest functions for each individual operator. In particular, the hybrid algorithms using CHM and SBM have optimal asymptotic performance on both OneMax and MinBlocks. We then investigate easiest functions for hybrid schemes and show that an easiest function for a hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator.

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Complexity Theory for Discrete Black-Box Optimization Heuristics

Doerr

2019

Natural Computing Series

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A predominant topic in the theory of evolutionary algorithms and, more generally, theory of randomized black-box optimization techniques is running time analysis. Running time analysis aims at understanding the performance of a given heuristic on a given problem by bounding the number of function evaluations that are needed by the heuristic to identify a solution of a desired quality. As in general algorithms theory, this running time perspective is most useful when it is complemented by a meaningful complexity theory that studies the limits of algorithmic solutions.In the context of discrete black-box optimization, several black-box complexity models have been developed to analyze the best possible performance that a black-box optimization algorithm can achieve on a given problem. The models differ in the classes of algorithms to which these lower bounds apply. This way, black-box complexity contributes to a better understanding of how certain algorithmic choices (such as the amount of memory used by a heuristic, its selective pressure, or properties of the strategies that it uses to create new solution candidates) influences performance.In this chapter we review the different black-box complexity models that have been proposed in the literature, survey the bounds that have been obtained for these models, and discuss how the interplay of running time analysis and black-box complexity can inspire new algorithmic solutions to well-researched problems in evolutionary computation. We also discuss in this chapter several interesting open questions for future work.

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From black-box complexity to designing new genetic algorithms

Cited by 204 publications

References 14 publications

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation

Complexity Theory for Discrete Black-Box Optimization Heuristics

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