Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Optimal capacity allocation in multi-auction electricity markets under uncertainty
Computers and Operations Research
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Genetic algorithms (GAs) have been argued to constitute a flexible search thereby enabling to solve difficult problems which classical optimization methodologies may find hard to solve. This paper is intended towards this direction and show a systematic application of a GA and its modification to solve a real-world optimization problem of sizing a solar thermal electricity plant. Despite the existence of only three variables, this problem exhibits a number of other common difficulties -- black-box nature of solution evaluation, massive multi-modality, wide and non-uniform range of variable values, and terribly rugged function landscape - which prohibits a classical optimization method to find even a single acceptable solution. Both GA implementations perform well and a local analysis is performed to demonstrate the optimality of obtained solutions. This study considers both classical and genetic optimization on a fairly complex yet typical real-world optimization problems and demonstrates the usefulness and future of GAs in applied optimization activities in practice.