Evolutionary optimization for computationally expensive problems

Despite all the appealing features of Evolutionary Algorithms (EAs), thousands of calls to the analysis or simulation codes are often required to locate a near optimal solution. Two major solutions for this issue are: 1) to use computationally less expensive surrogate models, and 2) to use parallel and distributed computers. In this thesis, model management frameworks utilizing a diverse set of surrogate models are proposed. The proposed Generalized Surrogate Memetic (GSM) framework aims to unify diverse set of data-fitting models synergistically in the evolutionary search. In particular, the GSM framework exploits both the positive and negative impacts of approximation errors in the surrogate models used. An extended management framework is also proposed for EAs using multi-scale models and demonstrated on two real-world examples. Experimental study performed using data-fitting and multi-scale models indicates that the proposed frameworks are capable of attaining reliable, high quality, and e±cient performance under a limited omputational budget. In what follows, possibilities for further acceleration of the evolutionary optimization life cycle through parallelization are also considered. When applied to small-scale, dedicated, and homogeneous computing nodes, this seems to be a formidable solution. However, in a large-scale computing farm such as the Grid, reality proves otherwise. In a Grid computing environment, which emphasizes on the seamless sharing of computing resources across institutions, heterogeneity of resources is inevitable. In such situation, conventional parallelization without considering the heterogeneity of computing resources is likely to produce ine±cient optimization. The latter part of this thesis summarizes our works on parallelizing evolutionary optimization in a heterogeneous Grid computing environment.DOCTOR OF PHILOSOPHY (SCE

Efficient hierarchical parallel genetic algorithms using grid computing

In this paper, we present an efficient Hierarchical Parallel Genetic Algorithm framework using Grid computing (GE-HPGA). The framework is developed using standard Grid technologies and has two distinctive features, 1) an extended GridRPC API to conceal the high complexity of Grid environment, and 2) a metascheduler for seamless resource discovery and selection. To assess the practicality of the framework, theoretical analysis on the possible speed-up offered is presented. Empirical study on GE-HPGA using a benchmark problem and a realistic aerodynamic airfoil shape optimization problem for diverse Grid environments having different communication protocols, cluster sizes, processing nodes, at geographically disparate locations also indicates that the proposed GE-HPGA using Grid computing offers a credible framework for providing significant speed-up to evolutionary design optimization in science and engineering

A Study on Metamodeling Techniques, Ensembles, and Multi-Surrogates in Evolutionary Computation

Surrogate-Assisted Memetic Algorithm(SAMA) is a hybrid evolutionary algorithm, particularly a memetic algorithm that employs surrogate models in the optimization search. Since most of the objective function evaluations in SAMA are approximated, the search performance of SAMA is likely to be affected by the characteristics of the models used. In this paper, we study the search performance of using different metamodeling techniques, ensembles, and multisurrogates in SAMA. In particular, we consider the SAMA-TRF, a SAMA model management framework that incorporates a trust region scheme for interleaving use of exact objective function with computationally cheap local metamodels during local searches. Four different metamodels, namel

Efficient Hierarchical Parallel Genetic Algorithms using Grid computing

Lim D, Ong Y-S, Jin Y, Sendhoff B, Lee B-S. Efficient Hierarchical Parallel Genetic Algorithms using Grid computing. Future Generation Computer Systems. 2007;23(4):658-670.In this paper, we present an efficient Hierarchical Parallel Genetic Algorithm framework using Grid computing (GE-HPGA). The framework is developed using standard Grid technologies, and has two distinctive features: (1) an extended GridRPC API to conceal the high complexity of the Grid environment, and (2) a metascheduler for seamless resource discovery and selection. To assess the practicality of the framework, a theoretical analysis of the possible speed-up offered is presented. An empirical study on GE-HPGA using a benchmark problem and a realistic aerodynamic airfoil shape optimization problem for diverse Grid environments having different communication protocols, cluster sizes, processing nodes, at geographically disparate locations also indicates that the proposed GE-HPGA using Grid computing offers a credible framework for providing a significant speed-up to evolutionary design optimization in science and engineering

Inverse multi-objective robust evolutionary design

Lim D, Ong Y-S, Jin Y, Sendhoff B, Lee BS. Inverse multi-objective robust evolutionary design. Genetic Programming and Evolvable Machines. 2006;7(4):383-404.In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently

Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty

Lim D, Ong Y-S, Lim M-H, Jin Y. Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty. In: Yang S, Ong Y-S, Jin Y, eds. Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007: 437-456.Many existing works for handling uncertainty in problem-solving rely on some form of a priori knowledge of the uncertainty structure. However, in real- ity, one may not always possess the necessary expertise or sufficient knowledge to identify suitable bounds of the uncertainty involved. Rather, it is more likely that specifications of the realistic performance desired are derived, which may be based on the maximum degradation tolerable or worst-case performance permissible in the final solution. In this chapter we present a Single/Multi-objective Inverse Robust Evolutionary (SMIRE) optimization methodology. In contrast to conventional for- ward robust optimization, an inverse approach based on non-probabilistic methods is introduced to avoid making possible erroneous assumptions about the uncertainty when insufficient field data exists for accurately estimating its structure. Further, since uncertainty is practically impossible to avoid, we consider the possible benefits as the uncertainty prevails by introducing an opportunity criterion in the inverse search scheme. Four inverse schemes are presented to include the different objec- tives possibly considered in robust evolutionary optimization. The inverse schemes are applied on synthetic test functions to illustrate their utility