Reoptimization times of evolutionary algorithms on linear functions under dynamic uniform constraints

Shi, Feng; Schirneck, Martin; Friedrich, Tobias; Kötzing, Timo; Neumann, Franz‐Josef

doi:10.1145/3071178.3071270

Cited by 16 publications

(22 citation statements)

References 16 publications

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“…In our experimental investigations, we start by examining the knapsack problem where all weights are set to one and vary the constraint bound. This matches the setting of the optimization of a linear function with a dynamic uniform constraint analyzed in [7]. Our experimental results match the theoretical ones obtained in this paper and show that the multi-objective approaches using a population to cater for dynamic changes significantly reduce the offline error that occurred during the run of the algorithms.…”

Section: Introductionsupporting

confidence: 86%

“…The other algorithm we consider in this paper is a multi-objective evolutionary algorithm (Algorithm 2), which is inspired by a theoretical study on the performance of evolutionary algorithms in the reoptimization of linear functions under dynamic uniform constraints [7]. Each solution x in the objective space is a two-dimensional point f M OEA (x) = (w(x), p(x)).…”

Section: Algorithmsmentioning

confidence: 99%

“…In the context of continuous optimization, dynamically changing constraints have been investigated in [2,5]. Theoretical investigations for combinatorial optimization problems with dynamically changing constraints have recently been carried out [6,7]. The goal of this paper is to contribute to this research direction from an experimental perspective.…”

Section: Introductionmentioning

confidence: 99%

“…Here σ is used to determine the magnitude of changes and large values of σ make larger changes more likely. We investigate different approaches analyzed theoretically with respect to their runtime behavior in [7]. The algorithms that we consider are a classical (1+1) EA and multi-objective approaches that are able to store infeasible solutions as part of the population in addition to feasible solutions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem

Roostapour

Neumann

2018

Parallel Problem Solving From Nature – PPSN XV

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Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study singleand multi-objective baseline evolutionary algorithms for the classical knapsack problem where the capacity of the knapsack varies over time.We establish different benchmark scenarios where the capacity changes every τ iterations according to a uniform or normal distribution. Our experimental investigations analyze the behavior of our algorithms in terms of the magnitude of changes determined by parameters of the chosen distribution, the frequency determined by τ and the class of knapsack instance under consideration. Our results show that the multi-objective approaches using a population that caters for dynamic changes have a clear advantage on many benchmarks scenarios when the frequency of changes is not too high.

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Section: Introductionsupporting

confidence: 86%

Section: Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem

Roostapour

Neumann

2018

Parallel Problem Solving From Nature – PPSN XV

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“…Evolutionary algorithms (EAs) have been applied to many combinatorial optimisation problems and proven to be very successful in solving complex optimisation problems [21,31,33,34,7]. Mutation operators and crossover operators are the core features of evolutionary algorithms that have been studied by many researchers in the last decades [2,3].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithms for the chance-constrained knapsack problem

Xie

Harper

Assimi

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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The chance-constrained knapsack problem is a variant of the classical knapsack problem where each item has a weight distribution instead of a deterministic weight. The objective is to maximize the total profit of the selected items under the condition that the weight of the selected items only exceeds the given weight bound with a small probability of α. In this paper, consider problem-specific single-objective and multi-objective approaches for the problem. We examine the use of heavy-tail mutations and introduce a problem-specific crossover operator to deal with the chance-constrained knapsack problem. Empirical results for single-objective evolutionary algorithms show the effectiveness of our operators compared to the use of classical operators. Moreover, we introduce a new effective multi-objective model for the chance-constrained knapsack problem. We use this model in combination with the problem-specific crossover operator in multiobjective evolutionary algorithms to solve the problem. Our experimental results show that this leads to significant performance improvements when using the approach in evolutionary multi-objective algorithms such as GSEMO and NSGA-II.

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