Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic algorithms using low-discrepancy sequences
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A language-mapping approach to action-oriented development of information systems
European Journal of Information Systems - Special issue: Action in language, organisations and information systems
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We report the formulation and implementation of a genetic algorithm to address multi-objective optimisation of solar gain to buildings with the goal of minimising energy consumption and hence limiting carbon emissions. Heuristic optimisation approaches hold significant promise to balance complex tradeoffs in building design; however the unique nature of each building optimization problem limits broader implementation. Parameter selection is very challenging with little or no correlation between different architectural configurations. We address this issue through 'calibration' on smaller scale problems with derivable optimal solutions. Various seeding, selection and fitness options were trialled, as well as different parameter values. The Pareto front of the global solution set was successfully reproduced for the calibration case. Varying climate produced no major change in the nature of the solution; however, building orientation forced reparameterization for an optimal solution. Future work will establish when calibration is useful, and aim to quantify the nature of the solution space.