The Whitehead Minimization problem is a problem of finding elements of the minimal length in the automorphic orbit of a given element of a free group. The classical algorithm of Whitehead that solves the problem depends exponentially on the group rank. Moreover, it can be easily shown that exponential blowout occurs when a word of minimal length has been reached and, therefore, is inevitable except for some trivial cases. In this paper we introduce a deterministic Hybrid search algorithm and its stochastic variation for solving the Whitehead Minimization problem. Both algorithms use search heuristics that allow one to find a length-reducing automorphism in polynomial time on most inputs and significantly improve the reduction procedure. The...
This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and noising ...
AbstractIn this paper a nondeterministic minimization algorithm is presented. A common feature of ra...
An important advantage of genetic algorithms (GAs) are their ease of use, their wide applicability, ...
AbstractThe Whitehead Minimization problem is a problem of finding elements of the minimal length in...
In this paper we discuss several heuristic strategies which allow one to solve the Whitehead’s minim...
The Whitehead minimization problem consists in finding a minimum size element in the automorphic orb...
Abstract. We describe a linear time probabilistic algorithm to recognize Whitehead minimal elements ...
The article deals with the problem of inhomogeneous minimax problem solution, what is typical of sch...
Many stochastic local search (SLS) methods rely on the manipulation of single solutions at each of t...
The problem of design optimization is of high industrial interest, and has been extensively studied ...
Combinatorial optimization problems can be found in many aspects ofmanufacturing, computer science, ...
International audienceMany stochastic local search (SLS) methods rely on the manipulation of single ...
In this paper a non-deterministic minimization algorithm is presented. A common feature of random se...
We consider optimization problems with a small implicitly denned feasible region, and with an object...
This work introduces a generalised hybridisation strategy which utilises the information sharing mec...
This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and noising ...
AbstractIn this paper a nondeterministic minimization algorithm is presented. A common feature of ra...
An important advantage of genetic algorithms (GAs) are their ease of use, their wide applicability, ...
AbstractThe Whitehead Minimization problem is a problem of finding elements of the minimal length in...
In this paper we discuss several heuristic strategies which allow one to solve the Whitehead’s minim...
The Whitehead minimization problem consists in finding a minimum size element in the automorphic orb...
Abstract. We describe a linear time probabilistic algorithm to recognize Whitehead minimal elements ...
The article deals with the problem of inhomogeneous minimax problem solution, what is typical of sch...
Many stochastic local search (SLS) methods rely on the manipulation of single solutions at each of t...
The problem of design optimization is of high industrial interest, and has been extensively studied ...
Combinatorial optimization problems can be found in many aspects ofmanufacturing, computer science, ...
International audienceMany stochastic local search (SLS) methods rely on the manipulation of single ...
In this paper a non-deterministic minimization algorithm is presented. A common feature of random se...
We consider optimization problems with a small implicitly denned feasible region, and with an object...
This work introduces a generalised hybridisation strategy which utilises the information sharing mec...
This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and noising ...
AbstractIn this paper a nondeterministic minimization algorithm is presented. A common feature of ra...
An important advantage of genetic algorithms (GAs) are their ease of use, their wide applicability, ...