Nicolas Monmarché

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.

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.

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.

Visual Clustering with Artificial Ants Colonies

In this paper, we propose a new model of the chemical recognition system of ants to solve the unsupervised clustering problem. The colonial closure mechanism allows ants to discriminate between nestmates and intruders by the mean of a colonial odour that is shared by every nestmate. In our model we associate each object of the data set to the odour of an artificial ant. Each odour is defined as a real vector with two components, that can be represented in a 2D-space of odours. Our method simulates meetings between ants according to pre-established behavioural rules, to ensure the convergence of similar odours (i.e. similar objects) in the same portion of the 2D-space. This provides the expected partition of the objects. We test our method against other well-known clustering method and show that it can perform well. Furthermore, our approach can handle every type of data (from numerical to symbolic attibutes, since there exists an adapted similarity measure) and allows one to visualize the dynamic creation of the nests. We plan to use this algorithm as a basis for a more sophisticated interactive clustering tool.

On Generating HTML Style Sheets with an Interactive Genetic Algorithm Based on Gene Frequencies

We present in this paper a new interactive method called Imagine that automatically generates style sheets for Web sites. This method aims at satisfying the artistic or aesthetic preferences of the user. This method uses a genetic algorithm to generate style sheets and to find in a search space one or several style sheets that will maximize the user satisfaction. This genetic algorithm is interactive: it generates style sheets, it displays them, and then it asks the user to select those which look the best. In this way, the search for an optimal sheet is guided by the answers provided by the user. Also, this algorithm uses non standard genetic operators based on gene frequencies. We present examples obtained with the actual prototype.

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.

MP3 Players and Audio Games: An Alternative to Portable Video Games Console for Visually Impaired Players

Both the evolutions of technologies and the increase of the Web download market have led to a democratisation of digital audio players throughout the world. Smaller and lighter, this media has become the best companion to listen his (her) favorite music everywhere. Indeed, thanks to the improvement of their performances, currently, they can be used to watch videos, to store data, etc. Among these new applications, it is imaginable to use them as a portable audio games console. Audio games are still the main solution for visually impaired players to play computer games. This paper introduces our contribution to elaborate an audio games console, accessible for visually impaired players, based on digital audio players.

A Clustering Algorithm Based on the Ants Self-Assembly Behavior

We have presented in this paper an ants based clustering algorithm which is inspired from the self-assembling behavior observed in real ants. These ants progressively become connected to an initial point called the support and then successively to other connected ants. The artificial ants that we have defined similarly build a tree where each ant represents a node/data. Ants use the similarities between the data in order to decide where to connect. We have tested our method on numerical databases (either artificial, real, and from the CE.R.I.E.S.). We show that AntTree improves the clustering process compared to the Kmeans algorithm and to AntClass, a previous approach for data clustering with artificial ants.