Abstract. This paper reports work investigating various evolutionary approaches to vertex cover (VC), a well-known NP-Hard optimization problem. Central to each of the algorithms is a novel encoding scheme for VC and related problems that treats each chromosome as a binary decision diagram. As a result, the encoding allows only a (guaranteed optimal) subset of feasible solutions. The encoding also incorporates features of a powerful traditional heuristic for VC that allow initial EA populations to be seeded in known promising regions of the search space. The resulting evolutionary algorithms have displayed exceptionally strong empirical performance on various vertex cover, independent set, and maximum clique problem classes.
This paper presents a new genetic algorithm for the minimum vertex cover problem. It uses interval v...
Evolutionary algorithms are general problem solvers that have been successfully used in solving comb...
Hybrid methods are very popular for solving problems from combinatorial optimization. In contrast, t...
Abstract: The problem of finding a minimum vertex cover is an NP hard optimization problem. Some app...
In this paper, we consider multi-objective evolutionary algorithms for the Vertex Cover problem in t...
AMS Subj. Classification: 90C27, 05C85, 90C59The topic is related to solving the generalized vertex c...
Extended AbstractUsing evolutionary algorithms to generate a diverse set of solutions where all of t...
Evolutionary algorithms have been frequently used to deal with dynamic optimization problems, but th...
Vertex cover is one of the best known NP-Hard combinatorial optimization problems. Experimental work...
Vertex cover is one of the best known NP-Hard combinatorial optimization problems. Experimental work...
In this paper an evolutionary approach to solving the generalized vertex cover problem (GVCP) is pre...
Hybrid methods are very popular for solving problems from combinatorial optimization. In contrast to...
this paper are used to compare the behavior of the genetic algorithm with the vercov heuristic. mvc...
Abstract — The vertex cover (VC) problem belongs to the class of Non Deterministic Polynomial time c...
Evolutionary multiobjective optimization for the classical vertex cover problem has been analysed in...
This paper presents a new genetic algorithm for the minimum vertex cover problem. It uses interval v...
Evolutionary algorithms are general problem solvers that have been successfully used in solving comb...
Hybrid methods are very popular for solving problems from combinatorial optimization. In contrast, t...
Abstract: The problem of finding a minimum vertex cover is an NP hard optimization problem. Some app...
In this paper, we consider multi-objective evolutionary algorithms for the Vertex Cover problem in t...
AMS Subj. Classification: 90C27, 05C85, 90C59The topic is related to solving the generalized vertex c...
Extended AbstractUsing evolutionary algorithms to generate a diverse set of solutions where all of t...
Evolutionary algorithms have been frequently used to deal with dynamic optimization problems, but th...
Vertex cover is one of the best known NP-Hard combinatorial optimization problems. Experimental work...
Vertex cover is one of the best known NP-Hard combinatorial optimization problems. Experimental work...
In this paper an evolutionary approach to solving the generalized vertex cover problem (GVCP) is pre...
Hybrid methods are very popular for solving problems from combinatorial optimization. In contrast to...
this paper are used to compare the behavior of the genetic algorithm with the vercov heuristic. mvc...
Abstract — The vertex cover (VC) problem belongs to the class of Non Deterministic Polynomial time c...
Evolutionary multiobjective optimization for the classical vertex cover problem has been analysed in...
This paper presents a new genetic algorithm for the minimum vertex cover problem. It uses interval v...
Evolutionary algorithms are general problem solvers that have been successfully used in solving comb...
Hybrid methods are very popular for solving problems from combinatorial optimization. In contrast, t...