The Neural Control of a Robot in the Conditions of Movable Obstacles

The proposed concept of robot control assisting uses a neural network, whose operation relies on the activation of neurons delimiting a path from the source to the target with evading movable obstacles The complexity of the control algorithm is O (n). The proposed adjustment of neuron sensitivity using a two-element pencils of planes passing over the shortest path of the robot makes it possible to obtain a set of solutions with simultaneous classification in terms of a very important path length criterion.Предложена концепция сопровождения управления роботом с использованием нейронной сети, работа которой основана на активизации нейронов, определяющих путь от исходной точки до цели с уклонением от подвижных препятствий. Сложность алгоритма управления составляет О (n). Предложенная настройка нейронной чувствительности с использованием двухэлементных пучков плоскостей, пересекающих кратчайший путь робота, позволяет получить множество решений с одновременной классификацией по критерию длины пути.Запропоновано концепцію супроводу управління роботом з використанням нейронної мережі, робота якої базується на активізації нейронів, що визначають шлях від вихідної точки до цілі з відхиленням від рухомих перешкод. Складність алгоритму управління складає On(). Запропоноване настроювання нейронної чутливості з використанням двоелементних пучків площин, що перетинають найкоротший шлях робота, дозволяє отримати велику кількість рішень з одночасною класифікацією за критерієм довжини шляху

Genetic-based unit commitment algorithm

The National Energy Policy Act of 1992 allows open access to transmission lines. The electric utility industry is in the transition from operating in a monopolistic environment to one that is less regulated. For an electric utility to operate in this new environment, a new algorithm is needed to optimally schedule generating units in the needed response time of an electric power broker. Past methods of unit commitment scheduling are either too computationally slow or do not produce optimal unit commitment schedules. Unit commitment scheduling is the problem of determining the optimal set of generating units within a power system to be used during the next one to seven days. Mathematically, unit commitment scheduling is a mixed integer problem typically with thousands of variables and a large, complex set of constraints;This dissertation investigates applying a genetic algorithm to the unit commitment scheduling problem. Genetic algorithms are an optimization technique based on the operations observed in natural selection and genetics. The resulting algorithm of this research has three attributes that make it very attractive for unit commitment scheduling. The first attribute is the algorithm can consistently find good unit commitment schedules in a reasonable amount of computation time. The second attribute is the algorithm can produce multiple unit commitment schedules in one execution. The last attribute is that the algorithm performance increases with the addition of true costed constraints. Results are given for three different utilities for 24 and 48 hour unit commitment schedules and are compared to DYNAMICS

A grammar-based technique for genetic search and optimization

The genetic algorithm (GA) is a robust search technique which has been theoretically and empirically proven to provide efficient search for a variety of problems. Due largely to the semantic and expressive limitations of adopting a bitstring representation, however, the traditional GA has not found wide acceptance in the Artificial Intelligence community. In addition, binary chromosones can unevenly weight genetic search, reduce the effectiveness of recombination operators, make it difficult to solve problems whose solution schemata are of high order and defining length, and hinder new schema discovery in cases where chromosome-wide changes are required.;The research presented in this dissertation describes a grammar-based approach to genetic algorithms. Under this new paradigm, all members of the population are strings produced by a problem-specific grammar. Since any structure which can be expressed in Backus-Naur Form can thus be manipulated by genetic operators, a grammar-based GA strategy provides a consistent methodology for handling any population structure expressible in terms of a context-free grammar.;In order to lend theoretical support to the development of the syntactic GA, the concept of a trace schema--a similarity template for matching the derivation traces of grammar-defined rules--was introduced. An analysis of the manner in which a grammar-based GA operates yielded a Trace Schema Theorem for rule processing, which states that above-average trace schemata containing relatively few non-terminal productions are sampled with increasing frequency by syntactic genetic search. Schemata thus serve as the building blocks in the construction of the complex rule structures manipulated by syntactic GAs.;As part of the research presented in this dissertation, the GEnetic Rule Discovery System (GERDS) implementation of the grammar-based GA was developed. A comparison between the performance of GERDS and the traditional GA showed that the class of problems solvable by a syntactic GA is a superset of the class solvable by its binary counterpart, and that the added expressiveness greatly facilitates the representation of GA problems. to strengthen that conclusion, several experiments encompassing diverse domains were performed with favorable results