Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
ModGen: a model generator for instrumentation analysis
Advances in Engineering Software
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Using computational intelligence and parallelism to solve an industrial design problem
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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In this article we present a Multi-Objective Genetic Algorithm for Initialization (MOGAI) that finds a starting sensor configuration for Observability Analysis (OA), this study being a crucial stage in the design and revamp of process-plant instrumentation. The MOGAI is a binary-coded genetic algorithm with a three-objective fitness function based on cost, reliability and observability metrics. MOGAI’s special features are: dynamic adaptive bit-flip mutation and guided generation of the initial population, both giving a special treatment to non-feasible individuals, and an adaptive genotypic convergence criterion to stop the algorithm. The algorithmic behavior was evaluated through the analysis of the mathematical model that represents an ammonia synthesis plant. Its efficacy was assessed by comparing the performance of the OA algorithm with and without MOGAI initialization. The genetic algorithm proved to be advantageous because it led to a significant reduction in the number of iterations required by the OA algorithm.