Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives

Multiple objective evolutionary algorithms;Missile warning receiver;Multiple objective evolutionary algorithms;Genetic programming;Optimization;Pareto optimal

Rohling, Gregory Allen ; Electrical and Computer Engineering

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Multiple objective evolutionary algorithms;    Missile warning receiver;    Multiple objective evolutionary algorithms;    Genetic programming;    Optimization;    Pareto optimal;   

Others  :  https://smartech.gatech.edu/bitstream/1853/4835/1/rohling_gregory_a_200412_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

This research augments current Multiple Objective Evolutionary Algorithms with methods that dramatically reduce the time required to evolve toward a region of interest in objective space.Multiple Objective Evolutionary Algorithms (MOEAs) are superior to other optimization techniques when the search space is of high dimension and contains many local minima and maxima. Likewise, MOEAs are most interesting when applied to non-intuitivecomplex systems. But, these systems are often computationally expensive to calculate. When these systems require independent computations to evaluate each objective, the computational expense grows with each additional objective. This method has developed methods that reduces the time required for evolution by reducing the number of objective evaluations, while still evolving solutions that are Pareto optimal. To date, all other Multiple Objective Evolutionary Algorithms (MOEAs) require the evaluation of all objectives before a fitness value can be assigned to an individual. The original contributions of this thesis are:1. Development of a hierarchical search space description that allows association of crossover and mutation settings with elements of the genotypic description.2. Development of a method for parallel evaluation of individuals that removes the need for delays for synchronization.3. Dynamical evolution of thresholds for objectives to allow partial evaluation of objectives for individuals.4. Dynamic objective orderings to minimize the time required for unnecessary objective evaluations.5.Application of MOEAs to the computationallyexpensive flare pattern design domain. 6. Application of MOEAs to the optimization of fielded missile warning receiver algorithms.7. Development of a new method of using MOEAs forautomatic design of pattern recognition systems.