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We present a diierent type of genetic algorithm called the Structured Genetic Algorithm (sGA) for the design of application-speciic neural networks. The novelty of this new genetic approach is that it can determine the network structures and their weights solely by an evolutionary process. This is made possible for sGA primarily due to its redundant genetic(More)
In this paper, we describe the application of a new type of genetic algorithm called the Structured Genetic Algorithm (sGA) for function optimization in nonstationary environments. The novelty of this genetic model lies primarily in its redundant genetic material and a gene activation mechanism which utilizes a multi-layered structure for the chromosome. In(More)
Future database applications will require significant improvements in performance beyond the capabilities of conventional disk based systems. This paper describes a new approach to database systems architecture, which is intended to take advantage of solid-state memory in combination with data compression to provide substantial performance improvements. The(More)
Unit commitment is a complex decision-making process because of multiple constraints which must not be violated while nding the optimal or a near-optimal commitment schedule. This paper discusses the application of genetic algorithms for determining short term commitment order of thermal units in power generation. The objective of the optimal commitment is(More)
This paper discusses the application of a new genetic search approach called the Structured Genetic Algorithm (sGA) for solving engineering optimization problems. The novelity of this genetic model lies in its hierarchical genomic structure and a gene activation mechanism in its chromosome. Simulation results exhibit its robustness in nding global optima.
In this paper, we describes the application of a Structured Genetic Algorithm for integrating the process of design and training neural networks for a speciic task. The importent feature of this genetic approach is that it can determine the network structures and their weights solely by an evolutionary process. The paper presents some experimental results(More)