Combine and conquer: an evolutionary hyper-heuristic approach for solving constraint satisfaction problems

Ortíz-Bayliss, José Carlos; Terashima-Marín, Hugo; Conant-Pablos, Santiago Enrique

doi:10.1007/s10462-016-9466-x

Cited by 21 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their results show the advantage of their approach when compared to some bespoke methods from literature, and this is because of the use of an adaptive memory mechanism, which contains a collection of the best solutions. Ortiz-Bayliss et al [ 15 ] applied a genetic algorithm (GA) with variable-length chromosomes to evolve selection hyperheuristics applied to real and synthetic constraint satisfaction problems, using seven heuristics. Their results confirm the robustness of their approach in unseen instances without loss of efficiency.…”

Section: State-of-the-artmentioning

confidence: 99%

Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

Olivas

Amaya

Ortíz-Bayliss

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Hyperheuristics rise as powerful techniques that get good results in less computational time than exact methods like dynamic programming or branch and bound. These exact methods promise the global best solution, but with a high computational time. In this matter, hyperheuristics do not promise the global best solution, but they promise a good solution in a lot less computational time. On the contrary, fuzzy logic provides the tools to model complex problems in a more natural way. With this in mind, this paper proposes a fuzzy hyperheuristic approach, which is a combination of a fuzzy inference system with a selection hyperheuristic. The fuzzy system needs the optimization of its fuzzy rules due to the lack of expert knowledge; indeed, traditional hyperheuristics also need an optimization of their rules. The fuzzy rules are optimized by genetic algorithms, and for the rules of the traditional methods, we use particle swarm optimization. The genetic algorithm will also reduce the number of fuzzy rules, in order to find the best minimal fuzzy rules, whereas traditional methods already use very few rules. Experimental results show the advantage of using our approach instead of a traditional selection hyperheuristic in 3200 instances of the 0/1 knapsack problem.

show abstract

Section: State-of-the-artmentioning

confidence: 99%

Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

Olivas

Amaya

Ortíz-Bayliss

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…A popular hyper-heuristic framework is based on genetic algorithms to produce selection hyper-heuristics [33], [85], [86]. From now on, we refer to it simply as GAHH.…”

Section: ) a Genetic Algorithm Framework For Heuristic Selectionmentioning

confidence: 99%

“…Their idea is to try and circumvent the No-Free-Lunch (NFL) theorem [32] by learning how to combine heuristics when solving a single instance of a problem. Hence, HHs seek to integrate simple and computationally cheap approaches for conquering a problem [33]. A basic difference between MHs and HHs is that the former explores the solution space of a problem, whereas the latter focuses on the solver space.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

View full text Add to dashboard Cite

Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyperheuristics have on combinatorial optimization problems.

show abstract

“…Throughout this work, we used hyper-heuristics that could select among these four single heuristics (Def, MaP, MPW, and MiW). We followed the hyper-heuristic model previously described in (Ortiz-Bayliss et al 2016), where a messy genetic algorithm finds a set of rules that determine when to use one particular heuristic, based on a set of features that characterize the current problem state. The idea in this model is to minimize the error associated to the set of rules.…”

Section: Available Solversmentioning

confidence: 99%

Evolutionary-based tailoring of synthetic instances for the Knapsack problem

et al. 2019

Self Cite

View full text Add to dashboard Cite

The assessment of strengths and weaknesses of a solver is often limited by the diversity of the cases where it is tested upon. As such, it is paramount to have a versatile tool which finds the problem instances where such a solver excels/fails. In this manuscript, we propose to use an evolutionary algorithm for creating this tool. To validate our approach, we conducted several tests on four heuristics for the knapsack problem. Although, the process can be extended to other domains with relatively few changes. The tests cover different sets of instances, both favoring the performance of one heuristic while hindering that of the remaining ones, and vice versa. To further test our evolutionary-based model, we also apply it on a recent approach that combines the strengths of different heuristics to improve its performance (usually referred to as a hyper-heuristic). We show that it is possible to tailor instances in which even this more complex model excels/fails. Throughout our approach, a researcher can test a solver under different kinds of scenarios, delving deeper into the conditions that make it perform well/poorly. Therefore, we recommend using the proposed approach as a means to grasp better insights about strengths and weaknesses of different solvers. Keywords Evolutionary computation • Knapsack problem • Instance generation List of abbreviations MIPLIB Mixed integer programming library Def Default heuristic MaP Max profit heuristic MPW Max profit per Weight heuristic MiW Minimum weight heuristic LHS Latin hyper-cube sampling HH Hyper-heuristic Communicated by V. Loia.

show abstract

Combine and conquer: an evolutionary hyper-heuristic approach for solving constraint satisfaction problems

Cited by 21 publications

References 30 publications

Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

Evolutionary-based tailoring of synthetic instances for the Knapsack problem

Contact Info

Product

Resources

About