Interactive Evolutionary Computation As we have seen in this course, EC is a powerful search paradigm. As we have seen in this course, EC is a powerful.

Slides:



Advertisements
Similar presentations
Design Process Design Process Gateway To Technology®
Advertisements

Interactive Evolutionary Computation Review of Applications Praminda Caleb-Solly Intelligent Computer Systems Centre University of the West of England.
Constraint Optimization We are interested in the general non-linear programming problem like the following Find x which optimizes f(x) subject to gi(x)
Interactive Evolutionary Computation
Particle Swarm Optimization (PSO)  Kennedy, J., Eberhart, R. C. (1995). Particle swarm optimization. Proc. IEEE International Conference.
Particle Swarm Optimization PSO was first introduced by Jammes Kennedy and Russell C. Eberhart in Fundamental hypothesis: social sharing of information.
© 2005 Prentice Hall6-1 Stumpf and Teague Object-Oriented Systems Analysis and Design with UML.
Biologically Inspired AI (mostly GAs). Some Examples of Biologically Inspired Computation Neural networks Evolutionary computation (e.g., genetic algorithms)
Particle Swarm Optimization Particle Swarm Optimization (PSO) applies to concept of social interaction to problem solving. It was developed in 1995 by.
1 Wendy Williams Metaheuristic Algorithms Genetic Algorithms: A Tutorial “Genetic Algorithms are good at taking large, potentially huge search spaces and.
Evolutionary Programming An Example Evolutionary Computation Procedure EC{ t = 0; Initialize P(t); Evaluate P(t); While (Not Done) { Parents(t) = Select_Parents(P(t));
Chapter 4 DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE
Introduction to Evolutionary Computation Evolutionary Computation is the field of study devoted to the design, development, and analysis is problem solvers.
Estimation of Distribution Algorithms Let’s review what have done in EC so far: We have studied EP and found that each individual searched via Gaussian.
Natural Computation: computational models inspired by nature Dr. Daniel Tauritz Department of Computer Science University of Missouri-Rolla CS347 Lecture.
Introduction to Computational Intelligence (Evolutionary Computation) Evolutionary Computation is the field of study devoted to the design, development,
D Nagesh Kumar, IIScOptimization Methods: M1L4 1 Introduction and Basic Concepts Classical and Advanced Techniques for Optimization.
SIMULATION. Simulation Definition of Simulation Simulation Methodology Proposing a New Experiment Considerations When Using Computer Models Types of Simulations.
Computational Thinking Related Efforts. CS Principles – Big Ideas  Computing is a creative human activity that engenders innovation and promotes exploration.
James Matte Nicole Calbi SUNY Fredonia AMTNYS October 28 th, 2011.
Universidad de los Andes-CODENSA The Continuous Genetic Algorithm.
Genetic Algorithms: A Tutorial
Particle Swarm Optimization Algorithms
Systems Analysis – Analyzing Requirements.  Analyzing requirement stage identifies user information needs and new systems requirements  IS dev team.
Prepared by Barış GÖKÇE 1.  Search Methods  Evolutionary Algorithms (EA)  Characteristics of EAs  Genetic Programming (GP)  Evolutionary Programming.
Genetic Algorithm.
Internet Based Information Sources on Urbanism - Tutorial - Authors: D. Milovanovic, D. S. Furundzic, yubc.net.
Introduction to AI Michael J. Watts
1 Paper Review for ENGG6140 Memetic Algorithms By: Jin Zeng Shaun Wang School of Engineering University of Guelph Mar. 18, 2002.
Slides are based on Negnevitsky, Pearson Education, Lecture 12 Hybrid intelligent systems: Evolutionary neural networks and fuzzy evolutionary systems.
10/6/2015 1Intelligent Systems and Soft Computing Lecture 0 What is Soft Computing.
Swarm Intelligence 虞台文.
CS440 Computer Science Seminar Introduction to Evolutionary Computing.
Scientific Writing Abstract Writing. Why ? Most important part of the paper Number of Readers ! Make people read your work. Sell your work. Make your.
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
(Particle Swarm Optimisation)
1 IE 607 Heuristic Optimization Particle Swarm Optimization.
Fuzzy Genetic Algorithm
1 “Genetic Algorithms are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions.
Controlling the Behavior of Swarm Systems Zachary Kurtz CMSC 601, 5/4/
Evolving the goal priorities of autonomous agents Adam Campbell* Advisor: Dr. Annie S. Wu* Collaborator: Dr. Randall Shumaker** School of Electrical Engineering.
Chapter 4 Decision Support System & Artificial Intelligence.
Project ECLIPSE.  The convergence of media and technology in a global culture is changing the way we learn about the world.
Genetic Algorithms An Example Genetic Algorithm Procedure GA{ t = 0; Initialize P(t); Evaluate P(t); While (Not Done) { Parents(t) = Select_Parents(P(t));
Societies of Hill-Climbers Before presenting SoHCs let’s first talk about Hill-Climbing in general. For this lecture we will confine ourselves to binary-
Biologically inspired algorithms BY: Andy Garrett YE Ziyu.
Interactive Evolution in Automated Knowledge Discovery Tomáš Řehořek March 2011.
Evolving RBF Networks via GP for Estimating Fitness Values using Surrogate Models Ahmed Kattan Edgar Galvan.
Genetic Algorithms. Solution Search in Problem Space.
Genetic Algorithms An Evolutionary Approach to Problem Solving.
March 1, 2016Introduction to Artificial Intelligence Lecture 11: Machine Evolution 1 Let’s look at… Machine Evolution.
 Negnevitsky, Pearson Education, Lecture 12 Hybrid intelligent systems: Evolutionary neural networks and fuzzy evolutionary systems n Introduction.
Swarm Intelligence. Content Overview Swarm Particle Optimization (PSO) – Example Ant Colony Optimization (ACO)
When Web 2.0 encounters with iEA hwchen. Outline Motivation Web 2.0 iWeb 2.0 Introduction of iEA Related work of iEA My research project Conclusion.
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Intelligent Exploration for Genetic Algorithms Using Self-Organizing.
Meta-heuristics Introduction - Fabien Tricoire
INFORMATION COMPRESSION, MULTIPLE ALIGNMENT, AND INTELLIGENCE
C.-S. Shieh, EC, KUAS, Taiwan
Design Process Design Process Gateway To Technology®
Design Process Design Process Gateway To Technology®
Multi-Objective Optimization
“Hard” Optimization Problems
Design Process Design Process Gateway To Technology®
Design Process Gateway To Technology®
Design Process Design Process Gateway To Technology®
Design Process Design Process Gateway To Technology®
Design Process Design Process Design Process Gateway To Technology®
Design Process Design Process Gateway To Technology®
Coevolutionary Automated Software Correction
Presentation transcript:

Interactive Evolutionary Computation As we have seen in this course, EC is a powerful search paradigm. As we have seen in this course, EC is a powerful search paradigm. As long as the user is able to develop an adequate evaluation function, the ECs studied so far will perform well. As long as the user is able to develop an adequate evaluation function, the ECs studied so far will perform well. However, what happens when on cannot develop an evaluation function that is an accurate closed- form mathematical equation? However, what happens when on cannot develop an evaluation function that is an accurate closed- form mathematical equation?

Interactive Evolutionary Computation Interactive Evolutionary Computation (IEC) is a welcomed alternative when: Interactive Evolutionary Computation (IEC) is a welcomed alternative when: An accurate evaluation function is difficult to develop, andAn accurate evaluation function is difficult to develop, and The user has an idea of what a good solution may look like.The user has an idea of what a good solution may look like.

Interactive Evolutionary Computation IECs have been used for wide variety of applications including [Takagi, H. (2001). “Interactive Evolutionary Computaton: Fusion of the Capabilities of EC Optimization and Human Evaluation”, Proceedings of the IEEE, pp , Vol. 89, No. 9, September, IEEE Press] : IECs have been used for wide variety of applications including [Takagi, H. (2001). “Interactive Evolutionary Computaton: Fusion of the Capabilities of EC Optimization and Human Evaluation”, Proceedings of the IEEE, pp , Vol. 89, No. 9, September, IEEE Press] : Graphic Art, Computer Graphics, Animation,Graphic Art, Computer Graphics, Animation, Music,Music, Editorial Design,Editorial Design, Industrial Design,Industrial Design, Face Image Generation,Face Image Generation, Speech Processing,Speech Processing, Hearing Aid Adaptation,Hearing Aid Adaptation, Database Retrieval,Database Retrieval, Data MiningData Mining Image Processing,Image Processing, Robotics,Robotics, etcetc

Interactive Evolutionary Computation Not all IECs are the same. There seems to be a continuum. Not all IECs are the same. There seems to be a continuum. EC IntensiveHuman Interaction Intensive Human/EC Collaborative Kennedy, Externalized Particle Swarm Takagi, Lund, etc Parmee

Interactive Evolutionary Computation IECs are not just limited to human evaluation. IECs are not just limited to human evaluation. Human evaluation functions are used to provide a fitness for individuals being evolved. Human evaluation functions are used to provide a fitness for individuals being evolved. Instead using human evaluation, human selection (algorithms) can be used. Instead using human evaluation, human selection (algorithms) can be used. Also, human guided procreation has been used in IECs as well. Also, human guided procreation has been used in IECs as well.

Interactive Evolutionary Computation Examples

Brian Carnahan’s IGA for Anthropomorphic Design (Overcome by Fumes) Brian Carnahan’s IGA for Anthropomorphic Design (Overcome by Fumes)

Interactive Distributed Evolutionary Algorithms (IDEAs) Interactive Evolutionary Computation has been shown to be a very powerful technique for solving design problems where the fitness function cannot be expressed as a closed form mathematical equation. Interactive Evolutionary Computation has been shown to be a very powerful technique for solving design problems where the fitness function cannot be expressed as a closed form mathematical equation. Research in the area of Distributed and Parallel Evolutionary Computation has been successfully used to speed up the evolutionary search. Research in the area of Distributed and Parallel Evolutionary Computation has been successfully used to speed up the evolutionary search.

Interactive Distributed Evolutionary Algorithms (IDEAs) Therefore, interactive distributed evolutionary computation (IDEC) holds a great deal of promise because: Therefore, interactive distributed evolutionary computation (IDEC) holds a great deal of promise because: Multiple users should be able to design artifacts more quickly than a single user (reducing user fatigue),Multiple users should be able to design artifacts more quickly than a single user (reducing user fatigue), Artifacts developed by multiple users will have a wider range of acceptance,Artifacts developed by multiple users will have a wider range of acceptance, By observing how humans interactively solve problems, we may gain a better understanding (heuristics?) of how to develop ‘intelligent’ ECs.By observing how humans interactively solve problems, we may gain a better understanding (heuristics?) of how to develop ‘intelligent’ ECs.

Interactive Distributed Evolutionary Algorithms (IDEAs)

An IDEA for Emoticon Design Procedure IDEA_Client{ t = 0; t = 0; Initialize Pop(t) // Randomly Generate 9 Emoticons; Initialize Pop(t) // Randomly Generate 9 Emoticons; Present Pop(t) to User; Present Pop(t) to User; While (Not Done) While (Not Done) { Allow_User_to_Select_An_Emoticon(e); Allow_User_to_Select_An_Emoticon(e); Allow_User_to_Select_A_Mutation_Op(o); Allow_User_to_Select_A_Mutation_Op(o); Send_to_Meme_Space(e); Send_to_Meme_Space(e); Receive_From_Meme_Space(m); Receive_From_Meme_Space(m); Parents(t) = {e, m}; Parents(t) = {e, m}; Offspring(t) = {Create_4_Mutants(e,o); Offspring(t) = {Create_4_Mutants(e,o); Create_3_Recombinants(e,m,o);} Create_3_Recombinants(e,m,o);} Pop(t+1) = Parents(t)  Offspring(t); Pop(t+1) = Parents(t)  Offspring(t); t = t + 1; t = t + 1; }}

An IDEA for Emoticon Design

An IDEA for Emoticon Design Representation of a Candidate Emoticon Representation of a Candidate Emoticon

Experiments For a proof of concept we conduct 3 simple experiments (where Meme Space = 2.5 x # of Networked Users) : For a proof of concept we conduct 3 simple experiments (where Meme Space = 2.5 x # of Networked Users) : Smiley FaceSmiley Face AngerAnger Hand-In-GearHand-In-Gear

Results

Results: Smiley Face Which is the Best Smiley Face? Which is the Best Smiley Face?

Results: Anger Which of these is the Best Anger Emoticon? Which of these is the Best Anger Emoticon?

Results: Hand-In-Gear Which of these is the Best Hand-In- Gear Emoticon Which of these is the Best Hand-In- Gear Emoticon

Conclusions Our results show that IDEA can be used to allow multiple users to interactively design emoticons. Our results show that IDEA can be used to allow multiple users to interactively design emoticons. Using C4.5, the emoticons can be separated into two groups. The differences between the two groups are statistically significant. Using C4.5, the emoticons can be separated into two groups. The differences between the two groups are statistically significant.

Discussion: Why Use an IDEA ? Not All Designers are the Same Not All Designers are the Same StartersStarters Middle RelieversMiddle Relievers Relievers (Finishers)Relievers (Finishers) Not All Designers Work at the Same Pace Not All Designers Work at the Same Pace Not All Designers Have the Same Gifts Not All Designers Have the Same Gifts Some designers may be better critics (selectors)Some designers may be better critics (selectors) Some designers may be more skilled with the evolutionary operatorsSome designers may be more skilled with the evolutionary operators Exploits Design Team Diversity Exploits Design Team Diversity Meme Space: Meme Space: Allows a form of Evolutionary BacktrackingAllows a form of Evolutionary Backtracking Allows more universal ideas to survive longerAllows more universal ideas to survive longer

IDEA Questions Some questions concerning the use of IDEAs are as follows: Some questions concerning the use of IDEAs are as follows: Given a design team of N users what is the most effective meme space size?Given a design team of N users what is the most effective meme space size? How do we make sure that faster users don’t overrun the meme space?How do we make sure that faster users don’t overrun the meme space? What is the best composition for a design team?What is the best composition for a design team?

Some Questions (cont.) How many designs should one receive from meme space on a given generation?How many designs should one receive from meme space on a given generation? How does this number change over the evolutionary process?How does this number change over the evolutionary process? Should meme space designs be crossed with user selected images?Should meme space designs be crossed with user selected images?

Some Questions (cont.) Should the amount of crossover change over the evolutionary process?Should the amount of crossover change over the evolutionary process? How can we detect and reduce user fatigue and frustration?How can we detect and reduce user fatigue and frustration?