Memetic algorithms for Cross-domain Heuristic Search

Abstract

Abstract-Hyper-heuristic Flexible Framework (HyFlex) is an interface designed to enable the development, testing and comparison of iterative general-purpose heuristic search algorithms, particularly selection hyper-heuristics. A selection hyper-heuristic is a high level methodology that coordinates the interaction of a fixed set of low level heuristics (operators) during the search process. The Java implementation of HyFlex along with different problem domains was recently used in a competition, referred to as Cross-domain Heuristic Search Challenge (CHeSC2011). CHeSC2011 sought for the best selection hyper-heuristic with the best median performance over a set of instances from six different problem domains. Each problem domain implementation contained four different types of operators, namely mutation, ruin-recreate, hill climbing and crossover. CHeSC2011 including the competing hyper-heuristic methods currently serves as a benchmark for hyper-heuristic research. Considering the type of the operators implemented under the HyFlex framework, CHeSC2011 could also be used as a benchmark to empirically compare the performance of appropriate variants of the evolutionary computation methods across a variety of problem domains for discrete optimisation. In this study, we investigate the performance and generality level of generic steady-state and transgenerational memetic algorithms which hybridize genetic algorithms with hill climbing across six problem domains of the CHeSC2011 benchmark.

A self-adaptive Multimeme Memetic Algorithm co-evolving utility scores to control genetic operators and their parameter settings

“…This instance is one of the cases where SSMMA was outperformed by MMA on average in Table 2. Again, in general the self-adaptive MAs outperform the static MAs ofÖzcan et al [53] in this domain, with SSMMA outperforming MMA in some instances and vice versa in others.…”

“…Their work observed that a selection hyper-heuristic based on a framework distinguishing between mutation and hill climbing operators outperforms traditional approaches which use all operators together. This framework used the idea of explicitly enforcing diversification and intensification processes in hyper-heuristics, in a similar way to other existing approaches such as iterated local search [38] and MAs.Özcan et al [53] evaluated the performance of two static MAs on the six problem domains of the HyFlex benchmark described above. The first approach, a Steady-State Memetic Algorithm (SSMA), is a traditional MA which applies a random crossover operator to two individuals from the population before applying a random mutation and hill-climbing operator to the resultant solution.…”

“…Both approaches are static MAs with external adaptation under the taxonomy of [50]. The experimental results in [53] showed that SSMA performs better than TGMA overall, however most of the single-point-based search se- …”

“…Due to the success of using a steady state approach as reported byÖzcan et al [53], the same replacement scheme is used in this study. …”