Algorithms (X,sigma,eta) : quasi-random mutations for Evolution Strategies

International audienceRandomization is an efficient tool for global optimization. We here define a method which keeps : - the order 0 of evolutionary algorithms (no gradient) ; - the stochastic aspect of evolutionary algorithms ; - the efficiency of so-called "low-dispersion" points ; and which ensures under mild assumptions global convergence with linear convergence rate. We use i) sampling on a ball instead of Gaussian sampling (in a way inspired by trust regions), ii) an original rule for step-size adaptation ; iii) quasi-monte-carlo sampling (low dispersion points) instead of Monte-Carlo sampling. We prove in this framework linear convergence rates i) for global optimization and not only local optimization ; ii) under very mild assumptions on the regularity of the function (existence of derivatives is not required). Though the main scope of this paper is theoretical, numerical experiments are made to backup the mathematical results. Algorithm XSE: quasi-random mutations for evolution strategies. A. Auger, M. Jebalia, O. Teytaud. Proceedings of EA'2005

A benchmark for cooperative coevolution

Cooperative co-evolution algorithms (CCEA) are a thriving sub-field of evolutionary computation. This class of algorithms makes it possible to exploit more efficiently the artificial Darwinist scheme, as soon as an optimisation problem can be turned into a co-evolution of interdependent sub-parts of the searched solution. Testing the efficiency of new CCEA concepts, however, it is not straightforward: while there is a rich literature of benchmarks for more traditional evolutionary techniques, the same does not hold true for this relatively new paradigm. We present a benchmark problem designed to study the behavior and performance of CCEAs, modeling a search for the optimal placement of a set of lamps inside a room. The relative complexity of the problem can be adjusted by operating on a single parameter. The fitness function is a trade-off between conflicting objectives, so the performance of an algorithm can be examined by making use of different metrics. We show how three different cooperative strategies, Parisian Evolution, Group Evolution and Allopatric Group Evolution, can be applied to the problem. Using a Classical Evolution approach as comparison, we analyse the behavior of each algorithm in detail, with respect to the size of the proble

Evaluation of Interactive Machine Learning Systems

International audienceThe evaluation of interactive machine learning systems remains a difficult task. These systems learn from and adapt to the human, but at the same time, the human receives feedback and adapts to the system. Getting a clear understanding of these subtle mechanisms of cooperation and co-adaptation is challenging. In this chapter, we report on our experience in designing and evaluating various interactive machine learning applications from different domains. We argue for coupling two types of validation: algorithm-centered analysis, to study the computational behaviour of the system; and human-centered evaluation, to observe the utility and effectiveness of the application for end-users. We use a visual analytics application for guided search, built using an interactive evolutionary approach, as an exemplar of our work. We argue that human-centered design and evaluation complement al-gorithmic analysis, and can play an important role in addressing the " black-box " effect of machine learning. Finally, we discuss research opportunities that require human-computer interaction methodologies, in order to support both the visible and hidden roles that humans play in interactive machine learning

The cooperative royal road: Avoiding Hitchhiking

We propose using the so called Royal Road functions as test functions for cooperative co-evolutionary algorithms (CCEAs). The Royal Road functions were created in the early 90's with the aim of demonstrating the superiority of genetic algorithms over local search methods. Unexpectedly, the opposite was found to be true. The research deepened our understanding of the phenomenon of hitchhiking where unfavorable alleles may become established in the population following an early association with an instance of a highly fit schema. Here, we take advantage of the modular and hierarchical structure of the Royal Road functions to adapt them to a co-evolutionary setting. Using a multiple population approach, we show that a CCEA easily outperforms a standard genetic algorithm on the Royal Road functions, by naturally overcoming the hitchhiking effect. Moreover, we found that the optimal number of sub-populations for the CCEA is not the same as the number of components that the function can be linearly separated into, and propose an explanation for this behavior. We argue that this class of functions may serve in foundational studies of cooperative co-evolution