Results 191 to 200 of about 7,687 (217)

Evolutionary Cross-Domain Hyper-Heuristics

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
Designing effective algorithms to solve computational problems is difficult and time-consuming. The standard methodology for designing such algorithms is “top-down”. This process breaks down large problems into more understood components and eventually identifies problem-specific operators that algorithms need to use to solve the given problem.
Patricia Ryser-Welch, Julian F. Miller, Shariar Asta   +2 more
openaire   +1 more source

Selection Perturbative Hyper-Heuristics

2018
Selection perturbative hyper-heuristics select which low-level perturbative heuristic to apply at each point of improvement to a given initial complete solution to a problem. The initial solution is usually created either randomly or using a constructive low-level heuristic.
Nelishia Pillay, Rong Qu
openaire   +1 more source

Generation Constructive Hyper-Heuristics

2018
In solving combinatorial optimization problems, a low-level constructive heuristic is used to create an initial solution, which forms a starting point for optimization techniques to solve the problem. These heuristics are problem dependent and are rules of thumb, manually derived based on human intuition.
Nelishia Pillay, Rong Qu
openaire   +1 more source

Markov chain hyper-heuristic (MCHH)

Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011
In this paper we present the Markov chain Hyper-heuristic (MCHH), a novel online selective hyper-heuristic which employs reinforcement learning and Markov chains to provide an adaptive heuristic selection method. Experiments are conducted to demonstrate the efficacy of the method and comparisons are made with standard heuristics, a random hyper ...
Kent McClymont, Edward C. Keedwell
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Introduction to Hyper-Heuristics

2018
Research into solving combinatorial optimization problems such as timetabling, vehicle routing and rostering problems has involved deriving techniques that improve the results obtained by existing techniques for known benchmark sets. These benchmark sets are made publicly available for performance comparisons of different techniques in solving these ...
Nelishia Pillay, Rong Qu
openaire   +1 more source

A hyper-heuristic clustering algorithm

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012
The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros
Chun-Wei Tsai, Huei-Jyun Song, Ming-Chao Chiang   +2 more
openaire   +1 more source

Cross-Domain Hyper-Heuristics

2018
Hyper-heuristics aim to provide heuristic algorithms of a higher level of generality that produce good results for all problems in a domain rather than just for one or two problem instances but poor results for the others. Cross-domain hyper-heuristics extend this scope of generality across domains.
Nelishia Pillay, Rong Qu
openaire   +1 more source

Advances in Hyper-Heuristics

2018
The previous chapters have introduced the four types of hyper-heuristics, presented the theoretical foundations and examined various applications of hyper-heuristics. This chapter provides an overview of some advanced topics and recent trends in hyper-heuristics, namely, hybrid hyper-heuristics, hyper-heuristics for automated design, automated design ...
Nelishia Pillay, Rong Qu
openaire   +1 more source

Evolutionary multilabel hyper-heuristic design

2017 IEEE Congress on Evolutionary Computation (CEC), 2017
Nowadays, heuristics represent a commonly used alternative to solve complex optimization problems. This, however, has given rise to the problem of choosing the most effective heuristic for a given problem. In recent years, one of the most used strategies for this task has been the hyper-heuristics, which aim at selecting/generating heuristics to solve ...
Alejandro Rosales-Perez, Andres E. Gutierrez-Rodriguez, Jose C. Ortiz-Bayliss, Hugo Terashima-Marin, Carlos A. Coello Coello   +4 more
openaire   +1 more source

Generative Hyper-heuristics

Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2022
Daniel R. Tauritz, John R. Woodward
openaire   +1 more source