Add to ORKGIn this thesis, a new general adaptive algorithm for solving a wide variety of NP-Complete combinatorial problems is developed. The new technique is called Stochastic Evolution (SE). The SE algorithm is applied to Network Bisection, Vertex Cover, Set Partition, Hamilton Circuit, Traveling Salesman, Linear Ordering, Standard Cell Placement, and Multi-way Circuit Partitioning problems. It is empirically shown that SE out-performs the more established general optimization algorithm, namely, Simulated Annealing.U of I OnlyETDs are only available to UIUC Users without author permissio
This Memorandum is a draft version of a review paper which will be submitted to the Bulletin of the ...
Stochastic evolution (StocE) is an evolutionary metaheuristic that has shown to achieve better solut...
Abstract. Evolution is particularly good at finding specific solutions, which are only valid for exa...
In this thesis, a new general adaptive algorithm for solving a wide variety of NP-Complete combinato...
This paper gives an overview of some stochastic optimization strategies, namely, evolution strategie...
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-par...
This paper discusses the parallelization of Stochastic Evolution (StocE) metaheuristic, for a distri...
This thesis is concerned with a novel algorithm, generalized from Chaotic Simulated Annealing (CSA) ...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
We introduce the new optimization method of Simulated Evolution (SE), which is designed to find near...
The paper discusses the parallelization of Stochastic Evolution metaheuristic, identifying effective...
Evolutionary algorithms (EAs) are stochastic optimization techniques based on the principles of natu...
Abstract Simulated Evolution (SimE) is an evolutionary metaheuristic that has pro-duced results comp...
Abstrncl- Evolutionary algorithms have been studied by several researchers for the design of digital...
This Memorandum is a draft version of a review paper which will be submitted to the Bulletin of the ...
Stochastic evolution (StocE) is an evolutionary metaheuristic that has shown to achieve better solut...
Abstract. Evolution is particularly good at finding specific solutions, which are only valid for exa...
In this thesis, a new general adaptive algorithm for solving a wide variety of NP-Complete combinato...
This paper gives an overview of some stochastic optimization strategies, namely, evolution strategie...
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-par...
This paper discusses the parallelization of Stochastic Evolution (StocE) metaheuristic, for a distri...
This thesis is concerned with a novel algorithm, generalized from Chaotic Simulated Annealing (CSA) ...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
We introduce the new optimization method of Simulated Evolution (SE), which is designed to find near...
The paper discusses the parallelization of Stochastic Evolution metaheuristic, identifying effective...
Evolutionary algorithms (EAs) are stochastic optimization techniques based on the principles of natu...
Abstract Simulated Evolution (SimE) is an evolutionary metaheuristic that has pro-duced results comp...
Abstrncl- Evolutionary algorithms have been studied by several researchers for the design of digital...
This Memorandum is a draft version of a review paper which will be submitted to the Bulletin of the ...
Stochastic evolution (StocE) is an evolutionary metaheuristic that has shown to achieve better solut...
Abstract. Evolution is particularly good at finding specific solutions, which are only valid for exa...