Hybridizing evolutionary algorithms with variable-depth search to overcome local optima

Algorithmica (New York)

Volumn 59, Issue 3, 2011, Pages 343-368

  Sudholt, Dirk ^a,b  

^a TU DORTMUND UNIVERSITY   (Germany)

^b INTERNATIONAL COMPUTER SCIENCE INSTITUTE   (United States)

Author keywords

Combinatorial optimization; Evolutionary algorithms; Hybridization; Iterated local search; Memetic algorithms; Runtime analysis; Simulated annealing

Indexed keywords

ACTIVE FIELD; COMBINATORIAL PROBLEM; EMPIRICAL EVIDENCE; EXPONENTIAL TIME; GLOBAL OPTIMUM; HIGH PROBABILITY; HYBRID ALGORITHMS; HYBRID EVOLUTIONARY ALGORITHM; HYBRID METAHEURISTICS; HYBRIDIZATION; ITERATED LOCAL SEARCH; LOCAL OPTIMA; LOCAL SEARCH; LOCAL SEARCH OPERATORS; MAX-SAT; MEMETIC ALGORITHMS; META HEURISTICS; MIN-CUT; POLYNOMIAL-TIME; RAPID DEVELOPMENT; RUNTIME ANALYSIS; SHARP CONTRAST; STRUCTURED PROBLEMS; THEORETICAL FOUNDATIONS; VARIABLE DEPTH SEARCH;

COMBINATORIAL OPTIMIZATION; HEURISTIC ALGORITHMS; MATHEMATICAL OPERATORS; MULTIOBJECTIVE OPTIMIZATION; POLYNOMIAL APPROXIMATION; SIMULATED ANNEALING;

EVOLUTIONARY ALGORITHMS;

EID: 79951671522     PISSN: 01784617     EISSN: 14320541     Source Type: Journal    
DOI: 10.1007/s00453-009-9384-2     Document Type: Article

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