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Introduction Design is a purposeful knowledge-based human activity whose aim is to create form which, when realized, satisfies the given intended purposes. 1 Design may be categorized as routine or non-routine with the latter further categorized as innovative or creative. The lesser the knowledge about existing relationships between the requirements and the form to satisfy those requirements, the more a design problem tends towards creative design. Thus, for non-routine design, a knowledge-lean methodology is necessary. Natural evolution has produced a large variety of forms well-suited to their environment suggesting that the use of an evolutionary approach could provide meaningful design solutions in a non-routine design environment. This work investigates the possibilities of using an evolutionary approach based on a genotype which represents design grammar rules for instructions on locating appropriate building blocks. A decomposition/aggregation hierarc

This paper describes a method for automatically generating unique input output (UIO) sequences for FSM conformance testing. UIOs are used in conformance testing to verify the end state of a transition sequence. UIO sequence generation is represented as a search problem and genetic algorithms are used to search this space. Empirical evidence indicates that the proposed method yields considerably better (up to 62 % better) results compared with random UIO sequence generation.

Personnel rostering problems are highly constrained resource allocation problems.

Abstract—Sesame is a software framework that aims at developing a modeling and simulation environment for the efficient design space exploration of heterogeneous embedded systems. Since Sesame recognizes separate application and architecture models within a single system simulation, it needs an explicit mapping step to relate these models for cosimulation. The design tradeoffs during the mapping stage, namely, the processing time, power consumption, and architecture cost, are captured by a multiobjective nonlinear mixed integer program. This paper aims at investigating the performance of multiobjective evolutionary algorithms (MOEAs) on solving large instances of the mapping problem. With two comparative case studies, it is shown that MOEAs provide the designer with a highly accurate set of solutions in a reasonable amount of time. Additionally, analyses for different crossover types, mutation usage, and repair strategies for the purpose of constraints handling are carried out. Finally, a number of multiobjective optimization results are simulated for verification. Index Terms—Design space exploration, evolutionary algorithms, mixed integer programming, multiobjective optimization, multiprocessor system-on-chip (SoC) design. I.

Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser, 1957; Bremermann, 1958; Holland, 1975). We start with a brief introduction to simple genetic algorithms and associated terminology. GAs encode

Artificial neural networks (ANNs) have shown to perform satisfactorily for pattern recognition tasks. It has also been shown that ANNs are superior to some of the classical statistical methods in pattern classification, but little is known how to design the ANN. A genetic algorithm (GA) based method can be used to determine the ANN architecture for a specific task. We describe the netGEN system which is an existing implementation of a GA evolving ANNs in parallel. A simple pattern recognition task is solved so as to demonstrate the performance of netGEN. 1 Introduction We describe a parallel system for the pattern extraction task by the ANN approach. Compared to statistical classifiers, such as Bayesian a posteriori classifiers, ANN classifiers have the important characteristic that no underlying distributional form for the class densities is assumed [Lip93]. Due to the independency from a priori statistical parameters and their inherent parallel nature, we decided to use ANNs to solv...

In recent years the functionality required of computer based control systems for safetycritical real-time applications has increased dramatically. Inevitably this has led to an explosion in the complexity ofsuchsystems and an understanding, in both academia and industry, that existing design methods are no longer adequate. One design issue that has traditionally been addressed in an ad hoc and rather simplistic manner is that of setting the topology of a distributed computer based control system. A topology consists of a con gured set of hardware and software units employed to ful l a set of logical control actions. A topology may employ multiple, possibly diverse, copies of these units to ensure that dependability, timing and functional requirements are met. A designer aims to determine the set of units to be employed and how they should be con gured. A maintainer aims to discover the e ect of a change in functionality, or the units employed, on the e ectiveness of an existing topology. Potentially there are a large number of alternative feasible topologies. Unfortunately, existing techniques rely on past experience and typically set a topology very early in the design process. At best only a fraction of the admissible topologies are considered and

Personnel rostering problems are highly constrained resource allocation problems.

This paper proposes four performance measures of a genetic algorithm (GA) which enable us to compare different GAs for an optimization problem and different choices of their parameters' values. The performance measures are defined in terms of observations in simulation, such as the frequency of optimal solutions, fitness values, the frequency of evolution leaps, and the number of generations needed to reach an optimal solution. We present a case study in which parameters of a GA for robot path planning was tuned and its performance was optimized through performance evaluation by using the measures. Especially, one of the performance measures is used to demonstrate the adaptivity of the GA for robot path planning. We also propose a process of systematic tuning based on techniques for the design of experiments. Keywords: Genetic algorithms, performance evaluation, tuning, experimental design, path planning, and mobile robot 1 INTRODUCTION In recent years, genetic algorithms (GAs) [2, ...