Results 21 to 30 of about 2,708 (158)
Finding large cliques in sparse semi-random graphs by simple randomized search heuristics [PDF]
Theoretical Computer Science, 2006 Surprisingly, general heuristics often solve some instances of hard combinatorial problems quite sufficiently, although they do not outperform specialized algorithms.
Storch, Tobias
core +4 more sources
Worst-case and Average-case Approximations by Simple Randomized Search Heuristics
, 2005 . In recent years, probabilistic analyses of algorithms have received increasing attention. Despite results on the average-case complexity and smoothed complexity of exact deterministic algorithms, little is known about the average-case behavior of ...
Carsten Witt
core +3 more sources
Toward a complexity theory for randomized search heuristics : black-box models [PDF]
, 2011 Randomized search heuristics are a broadly used class of general-purpose algorithms. Analyzing them via classical methods of theoretical computer science is a growing field. While several strong runtime bounds exist, a powerful complexity theory for such
Winzen, Carola, Winzen, C.
core +3 more sources
Theory of Randomized Search Heuristics [PDF]
Algorithmica, 2012 Randomized search heuristics such as evolutionary algorithms, evolution strategies, ant colony optimizers etc. are optimization algorithms that can be applied to a wide class of problems ranging from combinatorial to continuous optimization. They are popular in practice because they are generally easy to implement, their application requires little ...
Anne Auger, Carsten Witt
openaire +1 more source
Solving the mixed-model assembly line balancing problem type-I using a Hybrid Reactive GRASP
Production and Manufacturing Research: An Open Access Journal, 2022 One of the most recent challenges that manufacturers confront is to respond on time to the variety of customers’ demands for different products. The Assembly line is the main element responsible for assembling products in manufacturing systems, and it ...
Lakhdar Belkharroubi, Khadidja Yahyaoui
doaj +1 more source
Computing Minimum Cuts by Randomized Search Heuristics [PDF]
Algorithmica, 2008 We study the minimum s-t-cut problem in graphs with costs on the edges in the context of evolutionary algorithms. Minimum cut problems belong to the class of basic network optimization problems that occur as crucial subproblems in many real-world optimization problems and have a variety of applications in several different areas.
Frank Neumann 0001 +2 more
openaire +6 more sources
Commercial Territory Design for a Distribution Firm with New Constructive and Destructive Heuristics [PDF]
International Journal of Computational Intelligence Systems, 2012 A commercial territory design problem with compactness maximization criterion subject to territory balancing and connectivity is addressed. Four new heuristics based on Greedy Randomized Adaptive Search Procedures within a location-allocation scheme for ...
Jaime Cano-Belmán +2 more
doaj +1 more source
Theory of Randomized Search Heuristics [PDF]
, 2011 Randomized search heuristics such as evolutionary algorithms, genetic algorithms, evolution strategies, ant colony and particle swarm optimization turn out to be highly successful for optimization in practice. The theory of randomized search heuristics, which has been growing rapidly in the last five years, also attempts to explain the success of the ...
Anne Auger, Benjamin Doerr
openaire +3 more sources
Local search-based heuristics for the multiobjective multidimensional knapsack problem
Production, 2012 In real optimization problems it is generally desirable to optimize more than one performance criterion (or objective) at the same time. The goal of the multiobjective combinatorial optimization (MOCO) is to optimize simultaneously r > 1 objectives.
Dalessandro Soares Vianna +1 more
doaj +1 more source
Local search-based heuristics for the multiobjective multidimensional knapsack problem
Production, 2013 In real optimization problems it is generally desirable to optimize more than one performance criterion (or objective) at the same time. The goal of the multiobjective combinatorial optimization (MOCO) is to optimize simultaneously r > 1 objectives.
Dalessandro Soares Vianna +1 more
doaj +1 more source