Mohamed Slimane

On how pachycondyla apicalis ants suggest a new search algorithm

In this paper we present a new optimization algorithm based on a model of the foraging behavior of a population of primitive ants (Pachycondyla apicalis). These ants are characterized by a relatively simple but efficient strategy for prey search in which individuals hunt alone and try to cover a given area around their nest. The ant colony search behavior consists of a set of parallel local searches on hunting sites with a sensitivity to successful sites. Also, their nest is periodically moved. Accordingly, the proposed algorithm performs parallel random searches in the neighborhood of points called hunting sites. Hunting sites are created in the neighborhood of a point called nest. At constant intervals of time the nest is moved, which corresponds to a restart operator which re-initializes the parallel searches. We have applied this algorithm, called API, to numerical optimization problems with encouraging results.

On Improving Clustering in Numerical Databases with Artificial Ants

We present in this paper a new hybrid algorithm for data clustering. This algorithm discovers automatically clusters in numerical data without prior knowledge of a possible number of cleisses, without any initial partition, and without complex parameter settings. It uses the stochastic eind exploratory principles of an ant colony with the deterministic and heuristic principles of the K-means cJgorithm. Ants move on a 2D bosird and may load or drop objects. Dropping aa object on an existing heap of objects depends on the similarity between this object and the heap. The K-means algorithm improves the convergence of the ant colony clustering. We repeat two stochastic/deterministic steps and introduce hierarchical clustering on heaps of objects and not just objects. We also use other refinements such as aji heterogeneous population of ants to avoid complex parameters settings, and a local memory in each ant. We have applied this algorithm on standard databases cind we get very good results compared to the K-means and ISODATA algorithms.

A Critical and Empirical Study of Epistasis Measures for Predicting GA Performances: A Summary

Epistasis measures have been developed for measuring statistical information about the difficulty of a function to be optimized by a genetic algorithm (GA). We give first a review of the work on these measures such as the epistasis correlation. Then we try to relate the epistasis correlation to the overall performances of a binary GA on a set of 14 functions. The only relation that seems to hold strongly is that a high epistasis correlation implies GA-easy, as indicated by the GA theory of deceptiveness. Then, we show that changing the representation of the search space with transformations that improve epistasis measures does not imply the same increasing in the genetic algorithm performances. These both empirical studies seem to indicate that the generality of epistasis measures is limited.

Ants can play music

In this paper, we describe how we can generate music by simulating moves of artificial ants on a graph where vertices represent notes and edges represent possible transitions between notes. As ants can deposit pheromones on edges, they collectively build a melody which is a sequence of Midi events. Different parameter settings are tested to produce different styles of generated music with several instruments. We also introduce a mechanism that takes into account music files to initialize the pheromone matrix.

Optimizing Hidden Markov Models with a Genetic Algorithm

In this paper is presented the application of genetic algorithms (GAs) to the learning of hidden Markov models (HMMs). The Baum-Welch algorithm (BW), which optimizes the coefficients of a HMM, is improved by the use of a GA. The GA is able to find rapidly a good initial model compared to random generation, and this initial model is optimized further with BW. A representation and adapted genetic operators have been introduced in order to evolve matrix of probabilities. Several tests on artificial data show the interest in using a GA with BW.

Artificial Art Made by Artificial Ants

We present how we have considered the artificial ant paradigm as a tool for the generation of music and painting. From an aesthetic perspective, we are interested in demonstrating that swarm intelligence and self-organization can lead to spatio-temporal structures that can reach an artistic dimension. In our case, the use of artificial pheromones can lead to the creation of melodies thanks to a cooperative behavior of the ant-agents but also to the emergence of abstract paintings thanks to competitive behaviors within the artificial colonies. The user’s point of view is also taken into account through interactive genetic algorithms.

Epistasis for real encoding in genetic algorithms

Epistasis is a well known tool introduced by Davidor (1991) to understand and predict the performance of a genetic algorithm using binary encoding. The meaning of variance of epistasis is analyzed using Walsh basis; it is established that this variance can be viewed as a measure of the quality of a linear approximation. Variance of epistasis is therefore easier to estimate using the well known context of polynomial approximation regarding Euclidian distance. This approach also leads to the extension of variance of epistasis to real encoding without considering the notion of schema. Following this new definition, numerical analysis tools can also be applied to compute efficiently this generalized variance of epistasis.

Maintenance policy: degradation laws versus hidden Markov model availability indicator

Today, maintenance strategies and their analyses remain a worrying problem for companies. Socio-economic stakes depending on the competitiveness of each strategy are more than ever linked to the activity and quality of maintenance interventions. A series of specific events can eventually warn the expert of an imminent breakdown. This study aims at understanding such a signature thanks to hidden Markov models. For that purpose, two methods for damage level estimation of a maintained system are proposed. The first consists in using non-parametric and semi-parametric degradation laws (which will be used as references). The second method consists in using a Markovian approach. All proposals are illustrated on two studies corresponding to two real industrial situations (a continuous system for food processing and moulded products in aluminium alloys for the automotive industry).

Hidden Markov Models Training by a Particle Swarm Optimization Algorithm

In this work we consider the problem of Hidden Markov Models (HMM) training. This problem can be considered as a global optimization problem and we focus our study on the Particle Swarm Optimization (PSO) algorithm. To take advantage of the search strategy adopted by PSO, we need to modify the HMMs search space. Moreover, we introduce a local search technique from the field of HMMs and that is known as the Baum–Welch algorithm. A parameter study is then presented to evaluate the importance of several parameters of PSO on artificial data and natural data extracted from images.

Web Page Textual Color Contrast Compensation for CVD Users Using Optimization Methods

With this paper, we propose two methods for color contrast compensation of the textual information contained in a web page using numerical optimization. The optimization process can be reduced to the minimization of a single objective function which aims to achieve an on-the-fly compensation inducing a small amount of change in the original colors. Mass-spring system based optimization and CMA-ES metaheuristics are compared with the problem in order to assess their efficiency for compensating the loss. Experiments conducted on real and artificial datasets, prove the methods efficiency even with a small number of evaluations. Also, the methods behaviour is bound to the amount of compensation needed.