Efficiency enhancement techniques—such as parallelization and hybridization—are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once in every few iterations (generations), whereas in the remaining iterations, only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup, which decreases the asymptotic time complexity of model building in hBOA by a factor of Θ(n 0.26) to Θ(n 0.5), where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, if model building is the bottleneck, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. The paper also presents a dimensional model to provide a heuristic for scaling the structurebuilding period, which is the only parameter of the proposed sporadic model-building approach. The paper then tests the proposed method and the rule for setting the structure-building period on the problem of finding ground states of 2D and 3D Ising spin glasses.

The paper presents an evaluation-relaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended compact genetic algorithm is used to infer the structural form of the surrogate and a least squares method is used to estimate the coefficients of the surrogate. Using the surrogate avoids the need for expensive fitness evaluation for some of the solutions, and thereby yields significant efficiency enhancement. Results show that a surrogate, which automatically adapts to problem knowledge mined from probabilistic models, yields substantial speedup (1.75–3.1) on a class of boundedly-difficult additively-decomposable problems with and without additive Gaussian noise. The speedup provided by the surrogate increases with the number of substructures, substructure complexity, and noise-to-signal ratio.

Evolutionary Algorithms are largely used search and optimization procedures. They have been successfully applied for several problems and with proper care on the design process they can solve hard problems accurately, efficiently and reliably. The proper design of the algorithm turns some problems from intractable to tractable. We can go even further, using efficiency enhancements to turn them from tractable to practical. In this paper we show preliminary results of two efficiency enhancements proposed for Extended Compact Genetic Algorithm. First, a model building enhancement was used to reduce the complexity of the process from O(n 3) to O(n 2), speeding up the algorithm by 1000 times on a 4096 bits problem. Then, a local-search hybridization was used to reduce the population size by at least 32 times, reducing the memory and running time required by the algorithm. These results draw the first steps toward a competent and efficient Genetic Algorithm.

Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutionary computation. While solving a given problem with an EDA, the user has access to a set of models that reveal probabilistic dependencies between variables, an important source of information about the problem. However, as the complexity of the used models increases, the chance of overfitting and consequently reducing model interpretability, increases as well. This paper investigates the relationship between the probabilistic models learned by the Bayesian optimization algorithm (BOA) and the underlying problem structure. The purpose of the paper is threefold. First, model building in BOA is analyzed to understand how the problem structure is learned. Second, it is shown how the selection operator can lead to model overfitting in Bayesian EDAs. Third, the scoring metric that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection. Overall, this paper makes a contribution towards

The Bayesian optimization algorithm (BOA) uses Bayesian networks to learn linkages between the decision variables of an optimization problem. This paper studies the influence of different selection and replacement methods on the accuracy of linkage learning in BOA. Results on concatenated m-k deceptive trap functions show that the model accuracy depends on a large extent on the choice of selection method and to a lesser extent on the replacement strategy used. Specifically, it is shown that linkage learning in BOA is more accurate with truncation selection than with tournament selection. The choice of replacement strategy is important when tournament selection is used, but it is not relevant when using truncation selection. On the other hand, if performance is our main concern, tournament selection and restricted tournament replacement should be preferred. These results aim to provide practitioners with useful information about the best way to tune BOA with respect to structural model accuracy and overall performance.

This paper studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups

This paper studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.

Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for multiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo1−x) alloy show that GP predicts all barriers with 0.1 % error from calculations for less than 3 % of active

Abstract. The traditional representation of the populations used in evolutionary algorithms raises two types of problems: the loss of genetic diversity during the evolutionary process and evaluation of redundant individuals. In [11, 12] the authors propose a new formal model (PLATO) for multiset representation of individuals and their populations which applied to heuristic algorithms, minimizes the problems identified above. This paper presents a computational representation of populations based in multisets, and the adaptation of the genetic algorithm to deal with this type of representation, the Multiset Genetic Algorithm (MGA). A new operator called rescaling is developed as well as a metric to measure genetic diversity. The standard genetic algorithm is applied to some types of problems using the standard and the new type of populations and empirical results shows the genetic diversity is increased and the number of individuals evaluated is decreased as expected.

Abstract—ASHiCS permits the automatic discovery of high risk air traffic scenarios. In this paper we describe the evolutionary search used in ASHiCS and present an analysis of the project’s Stage 2 solution landscape. We suggest that random or exhaustive search is infeasible given the size of the solution space presented by simple air traffic scenarios, and that standard linear regression modeling is unlikely to find traffic input patterns that indicate the presence of high risk. While ASHiCS successfully targets high risk scenarios, we remain faced with very large search spaces in future Systems of Systems (SoS) models, and hope that our investigations into linear regression modeling will lead to a generic technique of practical value in the dimension reduction of these large search spaces. Keywords-component; search, safety, risk, heuristics, rare events, air traffic control simulation. I.