Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Parallel Computing - Special issue on cellular automata: from modeling to applications
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
A hybrid genetic algorithm for the multi-depot vehicle routing problem
Engineering Applications of Artificial Intelligence
Decision support for sustainable option selection in integrated urban water management
Environmental Modelling & Software
Parameter estimation of water quality model using particle swarm optimization technique
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Advanced Engineering Informatics
Engineering Applications of Artificial Intelligence
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Genetic algorithms are currently one of the state-of-the-art techniques for the optimisation of engineering systems including water network design and rehabilitation. They are capable of finding near optimal cost solutions to these problems given certain cost and hydraulic parameters. However, many forms of genetic algorithms rely on random starting points that are often poor solutions and the problem of how to efficiently provide good initial estimates of solution sets automatically is still an ongoing research topic. This paper proposes a novel method, known as CANDA-GA, which uses a heuristic-based, local representative cellular automata approach to provide a good initial population for genetic algorithm runs. CANDA-GA is applied to three networks, one taken from the literature and two taken from industry. The results show that the proposed method consistently outperforms the conventional non-heuristic-based GA approach in terms of producing more economically designed water distribution networks.