“Exploration and exploitation are the two cornerstones of problem solving by search.” For more than a decade, Eiben and Schippers' advocacy for balancing between these two antagonistic cornerstones still greatly influences the research directions of evolutionary algorithms (EAs) [1998]. This article revisits nearly 100 existing works and surveys how such works have answered the advocacy. The article introduces a fresh treatment that classifies and discusses existing work within three rational aspects: (1) what and how EA components contribute to exploration and exploitation; (2) when and how exploration and exploitation are controlled; and (3) how balance between exploration and exploitation is achieved. With a more comprehensive and systematic understanding of exploration and exploitation, more research in this direction may be motivated and refined.

Exploration and exploitation in evolutionary algorithms: A survey

Every search algorithm needs to address the exploration and exploitation of a search space. Exploration is the process of visiting entirely new regions of a search space, whilst exploitation is the process of visiting those regions of a search space within the neighborhood of previously visited points. In order to be successful a search algorithm needs to establish a good ratio between exploration and exploitation. In this respect Evolutionary Algorithms (EAs) [De Jong 2002; Eiben and Smith 2008], such as Genetic Algorithms (GAs) [Michalewicz 1996; Goldberg 2008], Evolutionary Strategies (ES) [Back 1996], Evolutionary Programming (EP) [Fogel 1999], and Genetic Programming (GP) [Koza 1992], to name the more well-known instances, are no exception. Herrera and Lozano [1996] emphasized this by saying “The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run.” Many researchers believe that EAs are effective because of their good ratio between exploration and exploitation. Michalewicz [1996] stated that “Genetic Algorithms are a

A Exploration and Exploitation in Evolutionary Algorithms: A Survey

A note on teaching-learning-based optimization algorithm

Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem

http://www.info.univ-angers.fr/pub/hao/papers/EJOR08.pdf

Adaptive Tabu Search for course timetabling

The goal of this paper is to discuss the tabu search (TS) meta-heuristic and its enhancement for combinatorial optimization problems. Firstly, the issues related to the principles and specific features of the standard TS are concerned. Further, a promising extension to the classical tabu search scheme is introduced. The most important component of this extension is a special kind of diversification mechanism. We give the paradigm of this new improved TS strategy, which is called an iterated tabu search (ITS). ITS was applied to the difficult combinatorial optimization problems, the traveling salesman problem (TSP) and the quadratic assignment problem (QAP). The results of the experiments with the TSP and QAP show the high efficiency of the ITS strategy. The outstanding performance of ITS is also demonstrated by the fact that the new record-breaking solutions were found for the hard QAP instances - tai80a and tai100a.

/pdf/iterated-tabu-search-an-improvement-to-standard-tabu-search-6f7rfjbtk9.pdf

Iterated tabu search: an improvement to standard tabu search

The maximally diverse grouping problem (MDGP) consists of finding a partition of a set of elements into a given number of mutually disjoint groups, while respecting the requirements of group size constraints and diversity. In this paper, we propose an iterated tabu search (ITS) algorithm for solving this problem. We report computational results on three sets of benchmark MDGP instances of size up to 960 elements and provide comparisons of ITS to five state-of-the-art heuristic methods from the literature. The results demonstrate the superiority of the ITS algorithm over alternative approaches. The source code of the algorithm is available for free download via the internet.

Maximally diverse grouping: an iterated tabu search approach

Given an undirected graph with costs associated both with its edges and unordered pairs of edges, the quadratic minimum spanning tree problem asks to find a spanning tree that minimizes the sum of costs of all edges and pairs of edges in the tree. We present multistart simulated annealing, hybrid genetic and iterated tabu search algorithms for solving this problem. We report on computational experiments that compare these algorithms on random graphs of size up to 50 vertices. The results indicate that the iterated tabu search algorithm is superior to the other two approaches in terms of both solution quality and computation time.

/pdf/metaheuristic-approaches-for-the-quadratic-minimum-spanning-1hv5lo88fx.pdf

Metaheuristic approaches for the quadratic minimum spanning tree problem

In this paper, an efficient hybrid genetic algorithm (HGA) and its variants for the well-known combinatorial optimization problem, the quadratic assignment problem (QAP) are discussed In particular, we tested our algorithms on a special type of QAPs, the structured quadratic assignment problems The results from the computational experiments on this class of problems demonstrate that HGAs allow to achieve near-optimal and (pseudo-)optimal solutions at very reasonable computation times The obtained results also confirm that the hybrid genetic algorithms are among the most suitable heuristic approaches for this type of QAPs

/pdf/testing-of-hybrid-genetic-algorithms-for-structured-3hpotlt49a.pdf

Testing of Hybrid Genetic Algorithms for Structured Quadratic Assignment Problems

Recently, genetic algorithms (GAs) are quite popular by solving combinatorial optimization problems. In this paper, we discuss a hybrid genetic algorithm that uses a new kind of solution recombination operators − a so-called multiple parent crossover. We examined this innovative crossover operator on the grey pattern problem, which is as special case of the well-known problem, the quadratic assignment problem. The results obtained during the experimentation with the set of 62 instances of the grey pattern problem demonstrate promising efficiency of the multiple parent crossover. All the instances tested were solved to pseudo-optimality within surprisingly small computation times.

/pdf/performance-of-hybrid-genetic-algorithm-for-the-grey-pattern-3xnuxmbcnh.pdf

Dalius Rubliauskas

Papers

Iterated tabu search: an improvement to standard tabu search

Maximally diverse grouping: an iterated tabu search approach

Metaheuristic approaches for the quadratic minimum spanning tree problem

Testing of Hybrid Genetic Algorithms for Structured Quadratic Assignment Problems

Performance of hybrid genetic algorithm for the grey pattern problem