Genetic algorithms for auto-tuning mobile robot motion control

This paper discusses a genetic algorithm (GA) based method for automatically tuning mobile robot motion controllers. The genetic algorithm evolves a controller that is optimised for a given performance measure. Genetic algorithms require a mapping from the genetic code to an implementation. This translation between the chromosome and the implementation allows the use of standard GA libraries, however the assumption constrains the types of problems that can be solved

Mining Frequent Itemsets Using Genetic Algorithm

In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in the discovery of frequent itemsets is that they perform global search and its time complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm

A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems

This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem

A new genetic algorithm for multi-label correlation-based feature selection.

This paper proposes a new Genetic Algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS). This GA performs a global search in the space of candidate feature subset, in order to select a high-quality feature subset is used by a multi-label classification algorithm - in this work, the Multi-Label k-NN algorithm. We compare the results of GA-ML-CFS with the results of the previously proposed Hill-Climbing for Multi-Label Correlation-Based Feature Selection (HC-ML-CFS), across 10 multi-label datasets

PDGA: The primal-dual genetic algorithm

Copyright @ 2003 IOS PressGenetic algorithms (GAs) are a class of search algorithms based on principles of natural evolution. Hence, incorporating mechanisms used in nature may improve the performance of GAs. In this paper inspired by the mechanisms of complementarity and dominance that broadly exist in nature, we present a new genetic algorithm — Primal-Dual Genetic Algorithm (PDGA). PDGA operates on a pair of chromosomes that are primal-dual to each other through the primal-dual mapping, which maps one to the other with a maximum distance away in a given distance space in genotype. The primal-dual mapping improves the exploration capacity of PDGA and thus its searching efficiency in the search space. To test the performance of PDGA, experiments were carried out to compare PDGA over traditional simple GA (SGA) and a peer GA, called Dual Genetic Algorithm (DGA), over a typical set of test problems. The experimental results demonstrate that PDGA outperforms both SGA and DGA on the test set. The results show that PDGA is a good candidate genetic algorithm

Genetic algorithm and neural network hybrid approach for job-shop scheduling

Copyright @ 1998 ACTA PressThis paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and the speed of calculation.This research is supported by the National Nature Science Foundation and National High -Tech Program of P. R. China

Mobile transporter path planning

The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research

Genetic algorithms applied to the scheduling of the Hubble Space Telescope

A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better