s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

:- Due to the stubborn nature of the deterministic job-shop scheduling problem many solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem specific strategies. We seek to assess a subclass of this problem in which the objective is minimising makespan, by providing an overview of the history, the techniques used and the researchers involved. The sense and direction of their work is evaluated by assessing the reported results of their techniques on the available benchmark problems. From these results the current situation and pointers for future work are provided. KEYWORDS:- Scheduling Theory; Job-Shop; Review; Computational Study; 1. INTRODUCTION Current market trends such as consumer demand for variety, shorter product life cycles and competitive pressure to reduce costs have resulted in the need for zero i...

. In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique -- mathematically known as "permutation with repetition" is presented. The main advantage of this single chromosome representation is -- in analogy to the permutation scheme of the traveling salesman problem (TSP) -- that it cannot produce illegal sets of operation sequences (infeasible symbolic solutions). As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually the GOX operator for permutations with repetition arises from a Generalisation of OX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX su...

: The Job-Shop Scheduling Problem (JSSP) is one of the most difficult NP-hard combinatorial optimization problems. This paper proposes a new method for solving JSSPs based on simulated annealing (SA), a stochastic local search, enhanced by shifting bottleneck (SB), a problem specific deterministic local search. In our method new schedules are generated by a variant of Giffler and Thompson's active scheduler with operation permutations on the critical path. SA selects a new schedule and probabilistically accepts or rejects it. The modified SB is applied to repair the rejected schedule; the new schedule is accepted if an improvement is made. Experimental results showed the proposed method found near optimal schedules for the difficult benchmark problems and outperformed other existing local search algorithms. Key Words: Simulated annealing, shifting bottleneck, job-shop scheduling, heuristics, local search 1. Background Scheduling is allocating shared resources over time to competi...

A great deal of research has been focused on solving the job-shop problem (P J ), over the last forty years, resulting in a wide variety of approaches. Recently, much effort has been concentrated on hybrid methods to solve P J as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that combine myopic problem specific methods and a meta-strategy which guides the search out of local optima. These approaches currently provide the best results. Such hybrid techniques are known as iterated local search algorithms or meta-heuristics. In this paper we seek to assess the work done in the job-shop domain by providing a review of many of the techniques used. The impact of the major contributions is indicated by applying these techniques to a set of standard benchmark problems. It is established that methods such as Tabu Search, Genetic Algorithms, Simulated Annealing should be considered complementary rather than competitive...

Abstract. In this paper, multi-step crossover (MSX) and a local search method are unified as a single operator called MSXF. MSX and MSXF utilize a neighborhood structure and a distance measure in the search space. In MSXF, a solution, initially set to be one of the parents, is stochastically replaced by a relatively good solution in the neighborhood, where the replacement is biased toward the other parent. After a certain number of iterations of this process, the best solution from those generated is selected as an offspring. Using job-shop scheduling problem benchmarks, MSXF was evaluated in a GA framework as a high-level crossover working on the critical path of a schedule. Experiments showed promising performance for the proposed method. 1

In this paper, a concept of degree of population diversity is introduced to quantitatively characterize and theoretically analyze the problem of premature convergence in genetic algorithms (GAs) within the framework of Markov chain. Under the assumption that the mutation probability is zero, the search ability of the GAs is discussed. It is proved that the degree of population diversity converges to zero with probability 1 so that the search ability of a genetic algorithm (GA) decreases and premature convergence occurs. Moreover, an explicit formula for the conditional probability of allele loss at a certain bit position is established to show the relationships between premature convergence and the GA parameters, such as population size, mutation probability, and some population statistics. The formula also partly answers the questions of to where a GA most likely converges. The theoretical results are all supported by the simulation experiments. Keywords: Genetic algorithms, Prematur...

Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population, using a number of parameterised genetic operators. In this thesis the phenomenon of Self Adaptation of the genetic operators is investigated. A new framework for classifying adaptive algorithms is proposed, based on the scope of the adaptation, and on the nature of the transition function guiding the search through the space of possible configurations of the algorithm. Mechanisms are investigated for achieving the self adaptation of recombination and mutation operators within a genetic algorithm, and means of combining them are investigated. These are shown to produce significantly better results than any of the combinations of fixed operators tested, across a range of problem types. These new operators reduce the need for the designer of an algorithm to select

Abstract — It is well known that GAs are not well suited for fine-tuning structures that are very close to optimal solutions and that it is essential to incorporate local search methods, such as neighborhood search, into GAs. This paper explores the use of a new GA operator called multi-step crossover fusion (MSXF), which combines a crossover operator with a neighborhood search algorithm. MSXF performs a local search essentially in the region within the search space between parent solutions to find a locally optimal solution that inherits the parents’ characteristics. GA/MSXF was applied to job-shop scheduling problem (JSSP). Experiments using benchmark problems show promising GA/MSXF performance even with a small population. I.

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...