The National Grid Company Plc. is responsible for the maintenance of the high voltage electricity transmission network in England and Wales. It must plan maintenance so as to minimize costs taking into account: ffl location and size of demand, ffl generator capacities and availabilities, ffl electricity carrying capacity of the remainder of the network, i.e. that part not undergoing maintenance. Previous work showed the combination of a Genetic Algorithm using an order or permutation chromosome combined with hand coded "Greedy" Optimizers can readily produce an optimal schedule for a four node test problem [Langdon, 1995]. Following this the same GA has been used to find low cost schedules for the South Wales region of the UK high voltage power network. This paper describes the evolution of the best known schedule for the base South Wales problem using Genetic Programming starting from the hand coded heuristics used with the GA. 1 Introduction In England and Wales electrical power...

In this paper we compare the effects of using various stochastic operators with the nonunicost set-covering problem. Four different crossover operators are compared to a repair heuristic which consists in transforming infeasible strings into feasible ones. These stochastic operators are incorporated in GENEsYs [2], the genetic algorithm we apply to problem instances of the set-covering problem we draw from well known test problems. GENEsYs uses a simple fitness function that has a graded penalty term to penalize infeasibly bred strings. The results are compared to a non GA-based algorithm based on the greedy technique. Our computational results are then compared, shedding some light on the effects of using different operators, a penalty function, and a repair heuristic on a highly constrained combinatorial optimization problem.

The maintenance of the high voltage electricity transmission network in England and Wales (the National Grid) is planned so as to minimise costs taking into account: -- location and size of demand for electricity, -- generator capacities and availabilities, -- electricity carrying capacity of the remainder of the network, i.e. that part not undergoing maintenance. This complex optimization and scheduling problem is currently performed manually (computerised viability checks can be performed after the schedule has been produced). This paper reports work aiming to automatically generate low cost schedules using genetic algorithms (GA). So far: -- A small demonstration problem has been identified, -- A fitness function has been devised, -- To date work has concentrated upon devising a representation based upon "greedy optimizers", which combine permutation GAs with scheduling heuristics, -- The best of these heuristics has been incorporated in the QGAME genetic algorit...

Abstract. Parallel implementationsof metaheuristicsappear quite naturally asan effective alternative to speed up the search for approximate solutions of combinatorial optimization problems. They not only allow solving larger problems or finding improved solutions with respect to their sequential counterparts, but they also lead to more robust algorithms. We review some trends in parallel computing and report recent results about linear speedups that can be obtained with parallel implementations using multiple independent processors. Parallel implementations of tabu search, GRASP, genetic algorithms, simulated annealing, and ant colonies are reviewed and discussed to illustrate the main strategies used in the parallelization of different metaheuristics and their hybrids. 1. Introduction. Although

In this paper, we study the efficacy of genetic algorithms in the context of combinatorial optimization. In particular, we isolate the effects of cross-over, treated as the central component of genetic search. We show that for problems of nontrivial size and difficulty, the contribution of cross-over search is marginal, both synergistically when run in conjunction with mutation and selection, or when run with selection alone, the reference point being the search procedure consisting of just mutation and selection. The latter can be viewed as another manifestation of the Metropolis process. Considering the high computational cost of maintaining a population to facilitate cross-over search, its marginal benefit renders genetic search inferior to its singletonpopulation counterpart, the Metropolis process, and by extension, simulated annealing. This is further compounded by the fact that many problems arising in practice may inherently require a large number of state transitions for a nea...

We present a combined use of Genetic Algorithms (GAs) and column generation to approximately solve graph-coloring problems. The proposed method is divided in two phases. The constructive phase builds the initial pool of columns using a Constructive Genetic Algorithm (CGA). Each column forms an independent set. The second phase solves by column generation the set covering formulation. The columns are generated solving weighted independent set problems. Some computational experience is given.

s: Jan. 1995 { Sep. 1998 ACM: ACM Guide to Computing Literature: 1979 - 1993/4 BA: Biological Abstracts: July 1996 - Aug. 1998 CA: Computer Abstracts: Jan. 1993 { Feb. 1995 CCA: Computer & Control Abstracts: Jan. 1992 { Apr. 1998 (except May-95) ChA: Chemical Abstracts: Jan. 1997 - Dec. 1998 CTI: Current Technology Index Jan./Feb. 1993 { Jan./Feb. 1994 DAI: Dissertation Abstracts International: Vol. 53 No. 1 { Vol. 56 No. 10 (Apr. 1996) EEA: Electrical & Electronics Abstracts: Jan. 1991 { Apr. 1998 EI A: The Engineering Index Annual: 1987 - 1992 EI M: The Engineering Index Monthly: Jan. 1993 { Apr. 1998 (except May 1997) N: Scientic and Technical Aerospace Reports: Jan. 1993 - Dec. 1995 (except Oct. 1995) P: Index to Scientic & Technical Proceedings: Jan. 1986 { May 1998 (except Nov. 1994) PA: Physics Abstracts: Jan. 1997 { Sep. 1998 1.1 Your contributions erroneous or missing? The bibliography database is updated on a regular basis and certain...

In this report we list diagonalisation routines available for parallel computers. The methodology of each routine is outlined together with benchmark results on a typical matrix where available. Storage requirements and advantages and disadvantages of the method are also compared. The vast majority of these routines are available for real dense symmetric matrices only, although there is a known requirement for other data types -- such as Hermitian or structured sparse matrices. We will report on new codes as they become available. This report is available from http://www.dl.ac.uk/TCSC/HPCI/ c fl1996, Daresbury Laboratory. We do not accept any responsibility for loss or damage arising from the use of information contained in any of our reports or in any communication about our tests or investigations. ii CONTENTS iii Contents 1 Summary 1 1.1 Test Results : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 Recommendations : : : : : : : : : : :...

Metaheuristic parallel search methods -- tabu search, simulated annealing and genetic algorithms, essentially -- are reviewed, classified and examined not according to particular methodological characteristics, but following the unifying approach of the level of parallelization. It is hoped that by examining the commonalities among parallel implementations across the field of metaheuristics, insights may be gained, trends may be discovered, and research challenges may be identified. Particular attention is paid to applications of parallel metaheuristics to transportation problems.

In this paper a new parallel genetic algorithm for coloring graph vertices is presented. In the algorithm we apply a migration model of parallelism and define two new recombination operators SPPX and CEX. For comparison two problem{ oriented crossover operators UISX and GPX are selected. The performance of the algorithm is veri ed by computer experiments on a set of standard graph coloring instances.