In this paper we propose an approach based on a genetic algorithm (GA) to design combinational logic circuits in which the objective is to minimize their total number of gates. Our results compare favorably against those produced by human designers and even another GA-based approach. We also briefly analyze the solutions found by the GA trying to find some clues on how it reduces a Boolean expression, and we indicate that such a reduction is achieved by reusing common patterns within the circuit in ways that are sometimes completely non-intuitive for a human designer. However, in small circuits, these patterns can be easier to detect and our approach could, therefore, be useful to teach circuit design since it can show students what steps to follow to simplify further a certain solution.

This paper presents an evolutionary algorithm with a new goal-sequence domination scheme for better decision support in multi-objective optimization. The approach allows the inclusion of advanced hard/soft priority and constraint information on each objective component, and is capable of incorporating multiple specifications with overlapping or non-overlapping objective functions via logical "OR" and "AND" connectives to drive the search towards multiple regions of trade-off. In addition, we propose a dynamic sharing scheme that is simple and adaptively estimated according to the on-line population distribution without needing any a priori parameter setting. Each feature in the proposed algorithm is examined to show its respective contribution, and the performance of the algorithm is compared with other evolutionary optimization methods. It is shown that the proposed algorithm has performed well in the diversity of evolutionary search and uniform distribution of non-dominated individuals along the final trade-offs, without significant computational effort. The algorithm is also applied to the design optimization of a practical servo control system for hard disk drives with a single voice-coil-motor actuator. Results of the evolutionary designed servo control system show a superior closed-loop performance compared to classical PID or RPT approaches.

We develop a new method to map (i.e. allocate and schedule) real-time applications into certain multiprocessor systems. Its objectives are 1) the minimization of the number of processors used and 2) the minimization of the deadline missing time. Given a parallel program with real time constraints and a multiprocessor system, our method finds schedules of the program in the system which satisfy all the real time constraints with minimum number of processors. The minimization is carried out through a Pareto-based Genetic Algorithm which independently considers the both goals, because they are non-commensurable criteria. Experimental results show that our scheduling algorithm achieved better performance than previous ones. The advantage of our method is that the algorithm produces not a single solution but a family of solutions known as the Paretooptimal set, out of which designers can select optimal solutions appropriate for their environmental conditions.

In this paper we propose a methodology based on a genetic algorithm (GA) to automate the design of combinational logic circuits in which we aim to minimize the total number of gates used. Our results are compared against those produced by human designers and by another GA-based approach. We also analyze the importance of using a non-binary representation in this problem despite the commonly accepted notion of universality of the binary representation in all kinds of GA-based applications. Keywords: circuit design, optimization, genetic algorithms, computeraided design, artificial intelligence. 1 Introduction Design is a task that requires knowledge and creativity which are two human attributes normally considered too complex to be automated. Researchers in Artificial Intelligence (AI) have devoted a lot of work towards automating different aspects of design, but most of the current results 1 consist of complex and expensive programs that can be easily outperformed by experienced h...

Abstract. In this paper, a new multiobjective genetic algorithm is employed to support the design of a hydraulic actuation system. First, the proposed method is tested using benchmarks problems gathered from the literature. The method performs well and it is capable of identifying multiple Pareto frontiers in multimodal function spaces. Secondly, the method is applied to a mixed variable design problem where a hydraulic actuation system is analyzed using simulation models. The design problem constitutes of a mixture of determining continuous variables as well as selecting components from catalogs. The multi-objective optimization results in a discrete Pareto front, which illustrate the trade-off between system cost and system performance. 1

Optimisation (MOO) has been focused on achieving the Pareto optimal front by explicitly analysing the dominance level of individual solutions. While such approaches have produced good results for a variety of problems, they are computationally expensive due to the complexities of deriving the dominance level for each solution against the entire population. TB_MOO (Threshold Based Multiobjective Optimisation) is a new artificial life approach to MOO problems that does not analyse dominance, nor perform any agent-agent comparisons. This reduction in complexity results in a significant decrease in processing overhead. Results show that TB_MOO performs comparably, and often better, than its more complicated counter-parts with respect to distance from the Pareto optimal front, but is slightly weaker in terms of distribution and extent. 1

Abstract: This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for multiobjective optimization of hypergraph partitioning. The main attention is focused on the incorporation of the Pareto optimality concept. We have modified the standard algorithm BOA for one criterion optimization according to well known niching techniques to find the Pareto optimal set. This approach was compared with standard weighting techniques and the single optimization approach with the constraint. The experiments are focused mainly on the bi-objective optimization because of the visualization simplicity.

In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are explored. For the multi–objective optimisation (MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are used and various techniques explored: weighted sums, lexicographic order, Pareto method with and without ranking, VEGA–like approaches etc. Large number of runs are performed for finding the optimal configuration and setting of the GA parameters. A novel method, weighted Pareto method is introduced and applied to a real–world optimisation problem. Decision support methods within conceptual engineering design framework are discussed and a new preference method developed. The preference method for translating vague qualitative categories (such as “more important”, “much less important ” etc.) into quantitative values (numbers) is based on fuzzy preferences and graph theory methods. Several applications of preferences are presented and discussed: ¯in weighted sum based optimisation methods; ¯in weighted Pareto method;

Genetic Algorithms have been successfully applied to numerous water resources problems, including problems with multiple objectives or uncertainty (noise). GAs tackle multi-objective optimization by following three basic principles - advancing the non-dominated frontier; maintaining diversity in the population (through various techniques like sharing, niching, and crowding); and using an elitist. However finding Pareto-optimal solutions becomes complicated when we add uncertainty to the problem. It was found that the solutions obtained using existing multi-objective solvers, although Pareto optimal were not the most robust or reliable solutions. In single-objective problems noise has typically been dealt with using Monte-Carlo-type sampling and some form of aggregate statistics (e.g., the average of the sample fitness). With multiple objectives the noise can interfere in determining non-domination of individuals, diversity preservation, and elitism (the three basic steps in multi-objective optimization). This paper proposes and tests several approaches to tackling some of these problems. These approaches strike a balance between finding the most optimal and the most reliable solution to the problem, thus giving decision makers and designers a practical and robust optimization tool.

A novel methodology is presented to structured yield-- aware synthesis. The trade--off between yield and the unspecified performances is explored along the design space boundaries, while respecting specifications on the other performances. Through the unique combination of multi--objective evolutionary optimization techniques, multi--variate regression modeling and sensitivity--based yield estimation, the designer is given access to this trade--off, all within transistor--level accuracy. Even more, a large reduction in required computer resources is obtained compared to alternative approaches.