Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
The RADIANCE lighting simulation and rendering system
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
IEA/AIE '99 Proceedings of the 12th international conference on Industrial and engineering applications of artificial intelligence and expert systems: multiple approaches to intelligent systems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Advanced Engineering Informatics
Pareto multi-criteria decision making
Advanced Engineering Informatics
Component-oriented decomposition for multidisciplinary design optimization in building design
Advanced Engineering Informatics
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
Advances and challenges in computing in civil and building engineering
Advanced Engineering Informatics
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There is a growing interest in integrating model based evolutionary optimization in engineering design decision making for effective search of the solution space. Most applications of evolutionary optimization are concerned with the search for optimal solutions satisfying pre-defined constraints while minimizing or maximizing desired goals. A few have explored post-optimization decision making using concepts such as Pareto optimality, but mostly in multi-objective problems. Sub-optimal solutions are usually discarded and do not contribute to decision making after optimization runs. However, the discarded 'inferior' solutions and their fitness contain useful information about underlying sensitivities of the system and can play an important role in creative decision making. The need for understanding the underlying system behavior is more pronounced in cases where variations in the genotype space can cause non-deterministic changes in either the fitness or phenotype space and where fitness evaluations are computationally expensive. The optimized design of an artificial lighting environment of a senior living room is used as a test case to demonstrate the need for and application of fitness visualization in genotype and phenotype spaces for effective decision making. Sub-optimal solutions are retained during optimization and visualized along with the optimum solution in a fitness array visualization system called phi-array, developed as part of this research. The optimization environment is based on genetic algorithm (GA) in which a compute-intensive raytracing rendering engine, RADIANCE, is used to evaluate the fitness of prospective design solutions. Apart from describing the development of the optimization system and demonstrating the utility of phi-array in effective decision making, this article explores optimization parameters and their effectiveness for artificial lighting design problems and the nature of their rugged fitness and constraint landscapes.