On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

Martins, Jean Paulo; Fonseca, Carlos M.; Delbem, Alexandre C. B.

doi:10.1016/j.neucom.2014.04.069

Cited by 29 publications

(15 citation statements)

References 61 publications

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“…This benchmark problem has been studied in both static (e.g. Shah and Reed (2011);Martins et al (2014)) and dynamic environments (e.g. Yang et al (2013) with different modifications.…”

Section: Dynamic Knapsack Problemmentioning

confidence: 99%

Adaptive-mutation compact genetic algorithm for dynamic environments

et al. 2016

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In recent years, the interest in studying nature inspired optimization algorithms for dynamic optimization problems (DOPs) has been increasing constantly due to its importance in real-world applications. Several techniques such as hyper-selection, change prediction, hypermutation and many more have been developed to address DOPs. Among these techniques, the hypermutation scheme has proved beneficial for addressing DOPs but requires that the mutation factors be picked a priori and this is one of the limitation of the hypermutation scheme.This paper investigates variants of the recently proposed adaptive-mutation compact genetic algorithm (amcGA). The amcGA is made up of a change detection scheme and mutation schemes, where the degree of change regulates the probability of mutation (i.e. the probability of mutation is directly proportional to the degree of change). This paper also presents a change trend scheme for the amcGA so as to boost its performance whenever a change occurs. Experimental results shows that the change trend and mutation schemes has an

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“…This benchmark problem has been studied in both static (e.g. Shah and Reed (2011);Martins et al (2014)) and dynamic environments (e.g. Yang et al (2013) with different modifications.…”

Section: Dynamic Knapsack Problemmentioning

confidence: 99%

Adaptive-mutation compact genetic algorithm for dynamic environments

et al. 2016

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“…We use the Hierarchical If-And-Only-If (HIFF) function and the Hierarchical Trap (HTRAP) function as decomposable problems [4] and the Hierarchically Dependent function (HDEP) [11], which we previously proposed, and N-K Landscape function (K = 4) (NKL-K4) [15] as problems with some overlaps among linkages, and the Multidimensional Knapsack Problem (MKP) [16] as a problem whose structure is hard to identify. As existing methods for comparison, we use the LTGA, which is expected to yield good search performance for decomposable problems, the extended compact genetic algorithm (ecGA) [13], which is one of the traditional PMBGAs using a marginal product model as a probabilistic model, and Chu and Beasley Genetic Algorithm (CBGA) [16], which was shown to have better performance for MKP than the LTGA [17], and the simple genetic algorithm (SGA) [18], which is one of the simplest operator-based GAs.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

Ohnishi¹,

Hamano²,

Koeppen³

2020

Electronics

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Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a machine learning technique to a population. Their central procedure is linkage detection that reveals a problem structure, that is, how the entire problem consists of sub-problems. However, the model-based evolutionary algorithms have been shown to be ineffective for problems that do not have relevant structures or those whose structures are hard to identify. Therefore, evolutionary algorithms that can solve both types of problems quickly, reliably, and accurately are required. The objective of the paper is to investigate whether the evolutionary algorithm evolving developmental timings (EDT) that we previously proposed can be the desired one. The EDT makes some variables values more quickly converge than the remains for any problems, and then, decides values of the remains to obtain a higher fitness value under the fixation of the variables values. In addition, factors to decide which variable values converge more quickly, that is, developmental timings are evolution targets. Simulation results reveal that the EDT has worse performance than the linkage tree genetic algorithm (LTGA), which is one of the state-of-the-art model-based evolutionary algorithms, for decomposable problems and also that the difference in the performance between them becomes smaller for problems with overlaps among linkages and also that the EDT has better performance than the LTGA for problems whose structures are hard to identify. Those results suggest that an appropriate search strategy is different between decomposable problems and those hard to decompose.

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“…This fact has motived the use of heuristic and metaheuristic methods to find approximate solutions. There are various approximate solution developments such as greedy ( Dantzig, 1957;Spielberg, 1969 ) and local search strategies ( Petersen, 1974 ), tabu search ( Glover & Kochenberger, 1996 ), simulated annealing ( Drexl, 1988 ), genetic algorithm ( Martins, Fonseca, & Delbem, 2014;Sakawa & Kato, 2003 ), ant colony ( Kong, Tian, & Kao, 2007 ), harmony search ( Zoua, Gaoa, Lib, & Wua, 2011 ) and artificial fish swarm ( Azad, Rocha, & Fernandes, 2014 ) algorithms. A particular advantage of many metaheuristics is the ability to efficiently perform global search, although there is no guarantee of finding a global solution.…”

Section: Introductionmentioning

confidence: 99%

Neuroevolution for solving multiobjective knapsack problems

Denysiuk

Gaspar‐Cunha

Delbem

2019

Expert Systems with Applications

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The multiobjective knapsack problem (MOKP) is an important combinatorial problem that arises in various applications, including resource allocation, computer science and finance. When tackling this problem by evolutionary multiobjective optimization algorithms (EMOAs), it has been demonstrated that traditional recombination operators acting on binary solution representations are susceptible to a loss of diversity and poor scalability. To address those issues, we propose to use artificial neural networks for generating solutions by performing a binary classification of items using the information about their profits and weights. As gradient-based learning cannot be used when target values are unknown, neuroevolution is adapted to adjust the neural network parameters. The main contribution of this study resides in developing a solution encoding and genotype-phenotype mapping for EMOAs to solve MOKPs. The proposal is implemented within a state-of-the-art EMOA and benchmarked against traditional variation operators based on binary crossovers. The obtained experimental results indicate a superior performance of the proposed approach. Furthermore, it is advantageous in terms of scalability and can be readily incorporated into different EMOAs.

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On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

Cited by 29 publications

References 61 publications

Adaptive-mutation compact genetic algorithm for dynamic environments

Adaptive-mutation compact genetic algorithm for dynamic environments

An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

Neuroevolution for solving multiobjective knapsack problems

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