Learning variable importance to guide recombination on many-objective optimization

International audienceThere are numerous many-objective real-world problems in various application domains for which it is difficult or time-consuming to derive Pareto optimal solutions. In an evolutionary algorithm, variation operators such as recombination and mutation are extremely important to obtain an effective solution search. In this paper, we study a machine learning-enhanced recombination that incorporates an intelligent variable selection method. The method is based on the importance of variables with respect to convergence to the Pareto front. We verify the performance of the enhanced recombination on benchmark test problems with three or more objectives using the many-objective evolutionary algorithm AϵSϵH as a baseline algorithm. Results show that variable importance can enhance the performance of many-objective evolutionary algorithms

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Evolutionary model type selection for global surrogate modeling

Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type

Genetic algorithms: a tool for optimization in econometrics - basic concept and an example for empirical applications

This paper discusses a tool for optimization of econometric models based on genetic algorithms. First, we briefly describe the concept of this optimization technique. Then, we explain the design of a specifically developed algorithm and apply it to a difficult econometric problem, the semiparametric estimation of a censored regression model. We carry out some Monte Carlo simulations and compare the genetic algorithm with another technique, the iterative linear programming algorithm, to run the censored least absolute deviation estimator. It turns out that both algorithms lead to similar results in this case, but that the proposed method is computationally more stable than its competitor. --Genetic Algorithm,Semiparametrics,Monte Carlo Simulation

A Study in function optimization with the breeder genetic algorithm

Optimization is concerned with the finding of global optima (hence the name) of problems that can be cast in the form of a function of several variables and constraints thereof. Among the searching methods, {em Evolutionary Algorithms} have been shown to be adaptable and general tools that have often outperformed traditional {em ad hoc} methods. The {em Breeder Genetic Algorithm} (BGA) combines a direct representation with a nice conceptual simplicity. This work contains a general description of the algorithm and a detailed study on a collection of function optimization tasks. The results show that the BGA is a powerful and reliable searching algorithm. The main discussion concerns the choice of genetic operators and their parameters, among which the family of Extended Intermediate Recombination (EIR) is shown to stand out. In addition, a simple method to dynamically adjust the operator is outlined and found to greatly improve on the already excellent overall performance of the algorithm.Postprint (published version