Study of the Fractal decomposition based metaheuristic on low-dimensional Black-Box optimization problems

This paper analyzes the performance of the Fractal Decomposition Algorithm (FDA) metaheuristic applied to low-dimensional continuous optimization problems. This algorithm was originally developed specifically to deal efficiently with high-dimensional continuous optimization problems by building a fractal-based search tree with a branching factor linearly proportional to the number of dimensions. Here, we aim to answer the question of whether FDA could be equally effective for low-dimensional problems. For this purpose, we evaluate the performance of FDA on the Black Box Optimization Benchmark (BBOB) for dimensions 2, 3, 5, 10, 20, and 40. The experimental results show that overall the FDA in its current form does not perform well enough. Among different function groups, FDA shows its best performance on Misc. moderate and Weak structure functions

Multiple auxiliary classifiers GAN for controllable image generation: Application to license plate recognition

Abstract One of the main challenges in developing machine learning (ML) applications is the lack of labeled and balanced datasets. In the literature, different techniques tackle this problem via augmentation, rendering, and over‐sampling. Still, these methods produce datasets that appear less natural, exhibit poor balance, and have less variation. One potential solution is to leverage the Generative Adversarial Network (GAN) which achieves remarkable results in the generation of high‐fidelity natural images. However, expanding the ability of GANs' to control generated image attributes with supervisory information remains a challenge. This research aims to propose an efficient method to generate high‐fidelity natural images with total control of its main attributes. Therefore, this paper proposes a novel Multiple Auxiliary Classifiers GAN (MAC‐GAN) framework based on Auxiliary Classifier GAN (AC‐GAN), multi‐conditioning, Wasserstein distance, gradient penalty, and dynamic loss. It is therefore presented as an efficient solution for highly controllable image synthesis red that allows to enrich and re‐balance datasets beyond data augmentation. Furthermore, the effectiveness of MAC‐GAN images on a target ML application called Automatic License Plate Recognition (ALPR) under limited resource constraints is probed. The improvement achieved is over 5% accuracy, which is mainly due to the ability of the MAC‐GAN to create a balanced dataset with controllable synthesis and produce multiple (different) images with the same attributes, thus increasing the variation of the dataset in a more elaborate way than data augmentation techniques

Deterministic metaheuristic based on fractal decomposition for large-scale optimization

International audienceIn this work a new method based on geometric fractal decomposition to solve large-scale continuous optimization problems is proposed. It consists of dividing the feasible search space into sub-regions with the same geometrical pattern. At each iteration, the most promising ones are selected and further decomposed. This approach tends to provide a dense set of samples and has interesting theoretical convergence properties. Under some assumptions, this approach covers all the search space only in case of small dimensionality problems. The aim of this work is to propose a new algorithm based on this approach with low complexity and which performs well in case of large-scale problems. To do so, a low complex method that profits from fractals properties is proposed. Then, a deterministic optimization procedure is proposed using a single solution-based metaheuristic which is exposed to illustrate the performance of this strategy. Obtained results on common test functions were compared to those of algorithms from the literature and proved the efficiency of the proposed algorithm