Comparison of Selection Methods in On-line Distributed Evolutionary Robotics

In this paper, we study the impact of selection methods in the context of on-line on-board distributed evolutionary algorithms. We propose a variant of the mEDEA algorithm in which we add a selection operator, and we apply it in a taskdriven scenario. We evaluate four selection methods that induce different intensity of selection pressure in a multi-robot navigation with obstacle avoidance task and a collective foraging task. Experiments show that a small intensity of selection pressure is sufficient to rapidly obtain good performances on the tasks at hand. We introduce different measures to compare the selection methods, and show that the higher the selection pressure, the better the performances obtained, especially for the more challenging food foraging task

Fine-grained Tournament Selection Operator in Genetic Algorithms

Tournament selection is one of the most popular selection operators in Genetic Algorithms. Recently, its popularity is increasing because this operator is well suited for Parallel Genetic Algorithms applications. In this paper, new selection operator is proposed. The new operator, which should be an improvement of the tournament selection, is named ``Fine-grained Tournament Selection'' (FGTS). It is shown that classical tournament selection is a special case of the FGTS and that new operator preserves its good features. Furthermore, theoretical estimations for the FGTS are made. Estimations for the FGTS are similar to those for the classical tournament selection. Finally, classical tournament selection, rank-based selection and FGTS are experimentally compared on a real world NP-hard problem and the obtained results are discussed

Simulating Sequential Search Models with Genetic Algorithms: Analysis of Price Ceilings, Taxes, Advertising and Welfare

This paper studies advertising, price ceilings and taxes in a sequential search model with bilateral heterogeneities in production and search costs. We estimate equilibria using a genetic algorithm (GA) applied to over 100 market scenarios, each differing based on the number of firms, number of consumers, existence of price ceilings or taxes, costs of production, costs of advertising, consumers' susceptibility to advertising and consumers' search costs. We compare our equilibrium results to those of the standard theoretical consumer search literature and analyze the welfare effects of advertising, price ceilings and sales taxes. We find that price ceilings and uninformative advertising can improve welfare, especially if search costs are sufficiently high.Sequential Search Models, Genetic Algorithms, Price Ceilings, Taxes, Advertising, Welfare

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

Deriving the dependence structure of portfolio credit derivatives using evolutionary algorithms

Even if the correct modeling of default dependence is essential for the valuation of portfolio credit derivatives, for the pricing of synthetic CDOs a one-factor Gaussian copula model with constant and equalpairwise correlationsfor all assets in the reference portfolio has become the standard market model. If this model were a re?ection of market opinion, there wouldn't be the implied correlation smilethatis observedinthe market. Thepurposeof thispaperistoderive a correlation structure from observed CDO tranche spreads. The correlation structure is chosen such that all tranche spreads of the traded CDO can be reproduced. This implied correlation structure can then be used to price o?-market tranches with the same underlying as the traded CDO. Using this approach we can significantly reduce the risk to misprice o?-market derivatives. Due to the complexity of the optimization problem we apply Evolutionary Algorithms. --