Experimental Aspects of Synthesis

We discuss the problem of experimentally evaluating linear-time temporal logic (LTL) synthesis tools for reactive systems. We first survey previous such work for the currently publicly available synthesis tools, and then draw conclusions by deriving useful schemes for future such evaluations. In particular, we explain why previous tools have incompatible scopes and semantics and provide a framework that reduces the impact of this problem for future experimental comparisons of such tools. Furthermore, we discuss which difficulties the complex workflows that begin to appear in modern synthesis tools induce on experimental evaluations and give answers to the question how convincing such evaluations can still be performed in such a setting.Comment: In Proceedings iWIGP 2011, arXiv:1102.374

On Method Overfitting

Benchmark problems should be hard. True. Methods for solving problems should be useful for more than just "beating" a particular benchmark. Truer still, we believe. In this paper, we examine the worth of the approach consisting of concentration on a particular set of benchmark problems, an issue raised by a recent paper by Ian Gent. We find that such a methodology can easily lead to publications of limited general use as far as our ability to solve practical problems is concerned. 1. The setting At the heart of the issue we address in this paper is a set of benchmark instances for the well-known Bin Packing Problem (BPP), defined in Garey and Johnson (1979). In 1994 we devised an optimization method for BPP, the Hybrid Grouping Genetic Algorithm (HGGA), issued from the marriage of the Grouping GA of Falkenauer (1998) and the Dominance Criterion of Martello and Toth (1990). In order to test the performance of our method, we decided to make a comparison with a well respected method, t..

A hybrid grouping genetic algorithm for bin packing

The Grouping Genetic Algorithm (GGA) is a Genetic Algorithm heavily modified to suit the structure of grouping problems. Those are the problems where the aim is to find a good partition of a set, or to group together the members of the set. The Bin Packing Problem (BPP) is a well known NP-hard grouping problem- items of various sizes have to be grouped inside bins of fixed capacity. On the other hand, the Reduction Method of Martello and Toth, based on their Dominance Criterion, constitutes one of the best OR techniques for optimization of the BPP to date. In this paper, we first describe the GGA paradigm as compared to the classic Holland-style GA and the ordering GA. We then show how the Bin Packing GGA can be enhanced with a local optimization inspired by the Dominance Criterion. An extensive experimental comparison shows that the combination yields an algorithm superior to either of its components

The grouping genetic algorithms and their industrial applications

Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

Applying Genetic Algorithms To Real-World Problems

This paper outlines what the author perceives as crucial ingredients of a successful application of Genetic Algorithms (gas) to real-world combinatorial problems. First, the importance of the Schema Theorem is stressed, pointing to crossover as the most potent force in a ga. Second, the importance of an encoding and operators adapted to the problem being solved is demonstrated, with two implications: the importance of the binary alphabet has been largely overstated in the past (in many problems it is not only unwarranted, it is detrimental), and practical gas must be built to solve problems (i.e., sets of instances) rather than (arbitrary) functions. The beneøts of the above guidelines are illustrated by the Grouping ga (gga), applied to three different grouping problems, namely Bin Packing and its variety Line Balancing, Equal Piles and Economies of Scale. The first application suggest a superiority of crossover-based search over a classic Branch&Bound, the second shows the superiori..

Integrated assembly and ressource planning in production line design

info:eu-repo/semantics/publishe

A note on the hierarchical nature of n-parent variation operators in evolutionary algorithms

Variation operators can be characterized by the probability mass that they associate with potential solutions from the state space of all possible solutions. Analysis is undertaken to show that the space of reachable probability mass functions is fundamentally hierarchical. The class of n-parent operators can generate a more diverse set of possible probabilistic searches of the state space than can be obtained by (n-1)-parent operators, or even a succession of (n-1)-parent operators. The result suggests that greater attention might be usefully applied in the exploration of multiparent variation operators. © 2002 Elsevier Science Inc. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Equipment selection and the line balancing with resource dependent tasks

This text aims to expand design and manufacturing issues to include teams and virtual enterprises which come together across space and time to develop new products and bring them to global markets.info:eu-repo/semantics/publishe