This paper describes the use of a genetic algorithm (GA) for the problem of offline point-to-point autonomous mobile robot path planning. The problem consists of generating "valid" paths or trajectories, for an Holonomic Robot to use to move from a starting position to a destination across a flat map of a terrain, represented by a two-dimensional grid, with obstacles and dangerous ground that the Robot must evade. This means that the GA optimizes possible paths based on two criteria: length and difficulty. First, we decided to use a conventional GA to evaluate its ability to solve this problem (using only one criteria for optimization). Due to the fact that we also wanted to optimize paths under two criteria or objectives, then we extended the conventional GA to implement the ideas of Pareto optimality, making it a multi-objective genetic algorithm (MOGA). We describe useful performance measures and simulation results of the conventional GA and of the MOGA that show that both types of GAs are effective tools for solving the point-to-point path-planning problem.
Abstract-Research on semantics in Genetic Programming(GP) has increased over the last number of years. Results in this area clearly indicate that its use in GP considerably increases performance. Many of these semantic-based approaches rely on a trial-and-error method that attempts to find offspring that are semantically different from their parents over a number of trials using the crossover operator (crossover-semantics based -CSB). This, in consequence, has a major drawback: these methods could evaluate thousands of nodes, resulting in paying a high computational cost, while attempting to improve performance by promoting semantic diversity. In this work, we propose a simple and computationally inexpensive method, named semantics in selection, that eliminates the computational cost observed in CSB approaches. We tested this approach in 14 GP problems, including continuous-and discrete-valued fitness functions, and compared it against a traditional GP and a CSB approach. Our results are equivalent, and in some cases, superior than those found by the CSB approach, without the necessity of using a "brute force" mechanism.
This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.
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