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Algoritmos Genéticos aula 2 - exemplos. Caixeiro viajante Exemplos de programa

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Apresentação em tema: "Algoritmos Genéticos aula 2 - exemplos. Caixeiro viajante Exemplos de programa"— Transcrição da apresentação:

1 Algoritmos Genéticos aula 2 - exemplos

2 Caixeiro viajante Exemplos de programa

3 Caixeiro viajante representação intuitiva: o próprio caminho problema: crossover DEBAHGFIC

4 Exemplo: recombinação de 2 pontos ABCDEFGHI DEBAHGFIC ABCAHGFHI DEBDEFGIC 12

5 PMX crossover (partially mapped) ABCDEFGHI DEBAHGFIC xxxAHGFxx xxxDEFGxx xBCAHGFxI xxBDEFGIC DBCAHGFEI AHBDEFGIC 12 34

6 OX crossover (ordered) ABCDEFGHI DEBAHGFIC xxxAHGFxx xxxDEFGxx CDEAHGFIB BAHDEFGIC partindo do segundo ponto de quebra, as cidades do outro pai são copiadas na mesma ordem, omitindo as cidades já presentes no filho

7 exemplos de mutação inversão: de duas cidades adjacentes inserção: seleciona uma cidade e a coloca aleatoriamente em algum ponto deslocamento: seleciona um sub-percurso e o coloca aleatoriamente em algum ponto troca recíproca: troca de posição entre duas cidades

8 2opt method AB e CD fazem parte do percurso se AB + CD > AC + BD então faço a troca para AC e BD A B C D A B C D

9 Trabalho de Jorge Meinhardt O presente trabalho pretende mostrar a possibilidade de uso de uma ferramenta de inteligência artificial utilizando a abordagem de algoritmo genético para auxílio ao projeto altimétrico de rodovias.

10 exemplo: robocup p/ melhorar o drible, para melhorar a condução da bola, etc...

11 An Evolutionary Behavior Tool for Reactive Multi-agent Systems Andre Zanki Cordenonsi Centro Universitário Franciscano Área de Ciências Exatas Rua dos Andradas 1614 Centro - Santa Maria – RS CEP Luis Otávio Alvares Universidade Federal do Rio Grande do Sul Instituto de Informática Av. Bento Gonçalves, 9500 Bairro Agronomia - Porto Alegre - RS -Brasil CEP Caixa Postal: 15064

12 Topics 1.Introduction 2.Artificial Life 3.The Simula++ 4.Genetic Operators 5.Food Foraging Problem 6.Final Considerations and Further Works

13 1. Introduction Multi-agent Systems: intelligent autonomous agents Reactive Multi-agent Systems: the perception/action approach Evolutionary Reactive Multi-agent Systems: agents can modify their behavior by a genetic evolutionary process

14 1. Introduction (…) Why to use an Evolutionary Reactive Multi-agent Systems ? -adaptation to dynamically environments -adaptation to unknown environments Objective of this paper: shows the specification and implementation of an Evolutionary Reactive Multi-agent Systems where the behavior of the agents can be modified during the simulation

15 1. Introduction (…) Previous work: the Simula Tool! [Frozza] A graphical tool to teach the multi-agent technology developed in the Instituto de Informática – UFRGS by Rejane Frozza and Luis Otávio Alvares A tool to simulate reactive multi-agent systems, using a graphical interface to build the agents behavior and to shows the simulation.

16 2. Artificial Life What is life ? The subject of : - Philosophy - Biology - Chemistry - History - …

17 2. Artificial Life (…) - … and Computer Science ! The Artificial Life IS : the understanding of life through the abstraction of its fundamental dynamic principles, and to create such dynamics in another physical medias, such as the computers Chistopher Langton

18 2. Artificial Life (…) The Emergent Behavior Complex/Organized behavior formed by the simple/local interactions among the agents Ex: ant colonies, assembly of cells

19 3. The Simula ++ -Reactive Multi-agent Systems -Didactic tool -Graphical User Interface

20 3. The Simula ++ (…) Class of agents and the Behavior Rule A Rule B Rule C Class X Rule A Rule B Rule C Agent 00 Rule A Rule B Rule C Agent 01 Rule A Rule B Rule C Agent 02 Initial State

21 3. The Simula ++ (…) Each Agent has - Set of Independent Elements - Chromosome ( set of rules )

22 3. The Simula ++ (…) Independent Elements ( equal for all agents of the same class ) - Initial Energy - Energy Amount - Maximum Life Time - Life Time - Sexual Maturity Time - New Generation Time

23 3. The Simula ++ (…) Chromosome: the rule characteristics - declarative - precondition - action - priority priority = 7 if (not perceive agent A) then random_move(); RULE A

24 3. The Simula ++ (…) The new Generation Algorithm LT == SMT New Offspring ! Execute Action reach NGT Fertile Agents yes no LT > SMT Insert Agent yes no

25 4. Genetic Operators One-Point External Crossover Two-Point External Crossover Rule A Rule B Rule C Agent 00 Rule E Rule F Rule G Agent 01 Rule A Rule F Rule G Offspring Rule A Rule B Rule C Agent 00 Rule E Rule F Rule G Agent 01 Rule A Rule F Rule C Offspring

26 4. Genetic Operators (…) Internal Crossover IF perceive Agent A AND not perceive Agent B THEN random move Agent 00 IF is Load THEN search base IF not perceive Agent A OR not perceive Agent B THEN search mine Agent 01 IF is not Load THEN search mine IF perceive Agent A OR not perceive Agent B THEN random move Offspring IF is not Load THEN search base

27 4. Genetic Operators (…) Mutation - between 1% and 2% - change pieces of a rule (respecting the semantic value) AND/OR Agent A, Agent B, Agent C, … perceive_agent, search_agent, escape_from_agent,…

28 5. Food Foraging Problem -Collect all minerals from an unknown environment -The environment is a grid ( 100 x 100 ) -Base is fixed -All agents known where is the base -There are three mines, with 100 units of mineral

29 5. Food Foraging Problem (…) It was defined six simulation groups: -Petit Poucet I [Drougol] -Petit Poucet II [Drougol] -Petit Poucet III [Drougol] -Dockers [Drougol] -Evolutionary Group -Standard Group

30 5. Food Foraging Problem (…) Standard Group (evolved by the evolutionary algorithm) IF NOT perceive (Mine) AND NOT perceive (Robot) THEN random move IF NOT perceive (Mine) AND perceive (Robot) THEN follows (Robot) IF NOT perceive (Mine) AND reach (Robot) THEN flee (Robot) IF perceive (Mine) THEN load AND return to base AND leave (Mark Track) IF perceive (Mark Track) THEN follows(Mark Track) AND remove(Mark Track)

31 E Group S Group Simulation steps Number of Robots PP1PP2PP3Doc.ES Time to collect all minerals Minimum time to collect all minerals Number of Robots for the minimum time Food Foraging Problem (…)

32 6. Conclusions and Further Works -Visual Analysis of the agent behavior -Discovery of new rules to know/unknown problems -Rapid prototype of RMAS -Paradigm change (use or not use the evolutionary algorithm)

33 6. Conclusions and Further Works (…) -Two fitness measure (global/local) -Automatic statistics and graphics -Implementation of different genetic operators

34 Bibliography DROGOUL, A. De la Simulation Multi-Agents à la Résolution Collective de Problèmes. Paris, France: Université Paris VI, Thèse de Doctorat. FROZZA, R. SIMULA – Ambiente para Desenvolvimento de Sistemas Multiagentes Reativos. Porto Alegre: CPGCC da UFRGS, Dissertação de mestrado.

35 De Jong e Otimização de Funções De Jong, em sua dissertação An Analisys of The Behavior of a class of Genetic Adaptive Systems,fez uma investigação cuidadosa de diversas variações do algoritmo genético simples aplicados à otimização de funções.

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