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• Session 1. Evolving digital circuits
• session 2. Artificial development
• session 3. GPU platforms for bio-inspired algorithms
• session 4. Implementations and applications of neural networks
• session 5. Test, repair and reconfiguration using evolutionary algorithms
• session 6. Applications of evolutionary algorithms in hardware
• session 7. Reconfigurable hardware platforms
• session 8. Applications of evolution to technology
• session 9. Novel methods in evolutionary design.

Annotation This book constitutes the refereed proceedings of the 9th International Conference on Evolvable Systems, ICES 2010, held in York, UK, in September 2010. The papers are organized in topical sections on evolving digital circuits; artificial development; GPU platforms for bio-inspired algorithms; implementations and applications of neural networks; test, repair and reconfiguration using evolutionary algorithms; applications of evolutionary algorithms in hardware; reconfigurable hardware platforms; applications of evolution to technology; and novel methods in evolutionary design.

• Computational Models of Evolution and Development
• Analysis of Gene Regulatory Networks
• Evolutionary Synthesis of Gene Regulatory Dynamics
• Evolution of Morphological Development
• Evolution of Neural Development
• Computational Brain-Body Co-Evolution
• Evolutionary Morphogenetic Self-Organization of Swarm Robots
• Towards Evolutionary Developmental Systems

This book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the authors extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence. Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence

• Intro
• Preface
• Contents
• Part I Background
• 1 Introduction
• 1.1 Optimization
• 1.2 Evolutionary Optimization
• 1.3 Evolutionary Multi-Task Optimization
• 1.4 Organization of the Book
• 2 Overview and Application-Driven Motivations of Evolutionary Multitasking
• 2.1 An Overview of EMT Algorithms
• 2.2 EMT in Real-World Problems
• 2.2.1 Category 1: EMT in Data Science Pipelines
• 2.2.2 Category 2: EMT in Evolving Embodied Intelligence
• 2.2.3 Category 3: EMT in Unmanned Systems Planning
• 2.2.4 Category 4: EMT in Complex Design

• 2.2.5 Category 5: EMT in Manufacturing, Operations Research
• 2.2.6 Category 6: EMT in Software and Services Computing
• Part II Evolutionary Multi-Task Optimization for Solving Continuous Optimization Problems
• 3 The Multi-Factorial Evolutionary Algorithm
• 3.1 Algorithm Design and Details
• 3.1.1 Multi-Factorial Optimization
• 3.1.2 Similarity and Difference Between Multi-factorial Optimization and Multi-Objective Optimization
• 3.1.3 The Multi-Factorial Evolutionary Algorithm
• 3.1.3.1 Population Initialization
• 3.1.3.2 Genetic Mechanisms
• 3.1.3.3 Selective Evaluation

• 3.1.3.4 Selection Operation
• 3.1.3.5 Summarizing the Salient Features of the MFEA
• 3.2 Empirical Study
• 3.2.1 Multitasking Across Functions with Intersecting Optima
• 3.2.2 Multitasking Across Functions with Separated Optima
• 3.2.3 Discussions
• 3.3 Summary
• 4 Multi-Factorial Evolutionary Algorithm with Adaptive Knowledge Transfer
• 4.1 Algorithm Design and Details
• 4.1.1 Representative Crossover Operators for Continuous Optimization
• 4.1.2 Knowledge Transfer via Different Crossover Operators in MFEA
• 4.1.3 MFEA with Adaptive Knowledge Transfer

• 4.1.3.1 Adaptive Assortative Mating and Adaptive Vertical Cultural Transmission
• 4.1.3.2 Adaptation of Transfer Crossover Indicators
• 4.2 Empirical Study
• 4.2.1 Experimental Setup
• 4.2.2 Performance Metric
• 4.2.3 Results and Discussions
• 4.2.3.1 Common Multi-Task Benchmarks
• 4.2.3.2 Complex Multi-Task Problems
• 4.2.4 Other Issues
• 4.3 Summary
• 5 Explicit Evolutionary Multi-Task Optimization Algorithm
• 5.1 Algorithm Design and Details
• 5.1.1 Denoising Autoencoder
• 5.1.2 The Explicit EMT Paradigm
• 5.1.2.1 Learning of Task Mapping

• 5.1.2.2 Explicit Genetic Transfer Across Tasks
• 5.2 Empirical Study
• 5.2.1 Single-Objective Multi-Task Optimization
• 5.2.1.1 Experiment Setup
• 5.2.1.2 Results and Discussions
• 5.2.2 Multi-Objective Multi-Task Optimization
• 5.2.2.1 Experiment Setup
• 5.2.2.2 Results and Discussions
• 5.3 Summary
• Part III Evolutionary Multi-Task Optimization for Solving Combinatorial Optimization Problems
• 6 Evolutionary Multi-Task Optimization for Generalized Vehicle Routing Problem with Occasional Drivers

A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brains ability to generalize in optimization particularly in population-based evolutionary algorithms have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems, each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.

Multi-objective optimization problems (MOPs) and uncertain optimization problems (UOPs) which widely exist in real life are challengeable problems in the fields of decision making, system designing, and scheduling, amongst others. Decomposition exploits the ideas of aEURO~making things simpleaEURO (TM) and aEURO~divide and conqueraEURO (TM) to transform a complex problem into a series of simple ones with the aim of reducing the computational complexity. In order to tackle the abovementioned two types of complicated optimization problems, this book introduces the decomposition strategy and conducts a systematic study to perfect the usage of decomposition in the field of multi-objective optimization, and extend the usage of decomposition in the field of uncertain optimization.
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• part 1. Theory and applications in engineering systems
• part 2. Theory and applications of single objective optimization studies
• part 3. Theory and applications of single and multiobjective optimization studies

• Methodology.- Memetic Algorithms in Planning, Scheduling, and Timetabling.- Landscapes, Embedded Paths and Evolutionary Scheduling.- Classical and Non-Classical Models of Production Scheduling.- Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes.- Designing Dispatching Rules to Minimize Total Tardiness.- A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling.- Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study.- An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem.- Timetabling.- Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem.- Metaheuristics for University Course Timetabling.- Energy Applications.- Optimum Oil Production Planning using an Evolutionary Approach.- A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems.- Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch.- Evolutionary Generator Maintenance Scheduling in Power Systems.- Networks.- Evolvable Fuzzy Scheduling Scheme for Multiple-ChannelPacket Switching Network.- A Multi-Objective Evolutionary Algorithm for Channel Routing Problems.- Transport.- Simultaneous Planning and Scheduling for Multi-Autonomous Vehicles.- Scheduling Production and Distribution of Rapidly Perishable Materials with Hybrid GA's.- A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities.- Business.- Evolutionary Optimization of Business Process Designs.- Using a Large Set of Low Level Heuristics in a Hyperheuristic Approach to Personnel Scheduling.- A Genetic-Algorithm-Based Reconfigurable Scheduler.- Evolutionary Algorithm for an Inventory Location Problem.
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Evolutionary scheduling is a vital research domain at the interface of artificial intelligence and operational research. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling. It demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.
(source: Nielsen Book Data)

• Parallel Evolutionary Optimization.- A Model for Parallel Operators in Genetic Algorithms.- Parallel Evolutionary Multiobjective Optimization.- Parallel Hardware for Genetic Algorithms.- A Reconfigurable Parallel Hardware for Genetic Algorithms.- Reconfigurable Computing and Parallelism for Implementing and Accelerating Evolutionary Algorithms.- Distributed Evolutionary Computation.- Performance of Distributed GAs on DNA Fragment Assembly.- On Parallel Evolutionary Algorithms on the Computational Grid.- Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit.- Parallel Particle Swarm Optimization.- Intelligent Parallel Particle Swarm Optimization Algorithms.- Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model.
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This book focuses on the aspects related to the parallelization of evolutionary computations, such as parallel genetic operators, parallel fitness evaluation, distributed genetic algorithms, and parallel hardware implementations, as well as on their impact on several applications. It offers a wide spectrum of sample works developed in leading research about parallel implementations of efficient techniques at the heart of computational intelligence.
(source: Nielsen Book Data)

• Part 1 - Setting the Stage
• Chapter 1 - Intelligent Machines: Imitating Life
• Chapter 2 - Deep Blue: A Triumph of AI?
• Chapter 3 - Building An Artificial Brain
• Chapter 4 - Evolutionary Computation: Putting Nature to Work
• Chapter 5 - Blue Hawaii: Natural Selection
• Chapter 6 - Checkers
• Chapter 7 - Chinook: The Man-machine Checkers Champion
• Chapter 8 - Samuel's Learning Machine
• Chapter 9 - The Samuel-Newell Challenge
• Part 2 - The Making of Blondie
• Chapter 10 - Evolving in the Checkers Environment
• Chapter 11 - In The Zone
• Chapter 12 - A Repeat Performance
• Chapter 13 - A New Dimension
• Chapter 14 - Letting the Genie Out of the Bottle
• Chapter 15 - Blondie24 Epilogue: The Future of Artificial Intelligence Appendix: Your Honor, I Object! Notes Index About the Author.
• (source: Nielsen Book Data)

Blondie24 tells the story of a computer that taught itself to play checkers far better than its creators ever could by using a program that emulated the basic principles of Darwinian evolution--random variation and natural selection-- to discover on its own how to excel at the game. Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn't have access to strategies employed by human grand masters, or to databases of moves for the endgame moves, or to other human expertise about the game of chekers. With only the most rudimentary information programmed into its "brain, " Blondie24 (the program's Internet username) created its own means of evaluating the complex, changing patterns of pieces that make up a checkers game by evolving artificial neural networks---mathematical models that loosely describe how a brain works. It's fitting that Blondie24 should appear in 2001, the year when we remember Arthur C. Clarke's prediction that one day we would succeed in creating a thinking machine. In this compelling narrative, David Fogel, author and co-creator of Blondie24, describes in convincing detail how evolutionary computation may help to bring us closer to Clarke's vision of HAL. Along the way, he gives readers an inside look into the fascinating history of AI and poses provocative questions about its future.
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• Preface to the Second Edition. Preface to the First Edition. Acknowledgments. Defining Artificial Intelligence. Natural Evolution. Computer Simulation of Natural Evolution. Theoretical and Empirical Properties of Evolutionary Computation. Intelligent Behavior. Perspective. Glossary. Index. About the Author.
• (source: Nielsen Book Data)

"In this revised and significantly expanded second edition, distinguished scientist Dr. David B. Fogel presents the latest advances in both the theory and practice of evolutionary computation to help you keep pace with the most recent developments in this fast-changing field. In-depth and updated, EVOLUTIONARY COMPUTATION shows you how to use simulated evolution to achieve machine intelligence. You will gain current insights into the history of evolutionary computation and the newest theories shaping research today. Fogel thoroughly reviews the ""no free lunch"" theorem and includes a discussion of findings that challenge the very foundations of simulated evolution. This second edition also presents the latest game-playing techniques that combine evolutionary algorithms with neural networks, including their success in playing competitive checkers. Chapter by chapter, this comprehensive book highlights the relationship between learning and intelligence. EVOLUTIONARY COMPUTATION features an unparalleled integration of history with state-of-the-art theory and practice for engineers, professors, and graduate students of evolutionary computation and computer science who need to keep up to date in this developing field." Sponsored by: IEEE Neural Networks Council.
(source: Nielsen Book Data)

"Science has made great strides in modeling space, time, mass and energy. Yet little attention has been paid to the precise representation of the information ubiquitous in nature. Introduction to Evolutionary Informatics fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits. Built on the foundation of a series of peer-reviewed papers published by the authors, the book is written at a level easily understandable to readers with knowledge of rudimentary high school math. Those seeking a quick first read or those not interested in mathematical detail can skip marked sections in the monograph and still experience the impact of this new and exciting model of nature's information. This book is written for enthusiasts in science, engineering and mathematics interested in understanding the essential role of information in closely examined evolution theory."--Publisher's website.

• Part I Foundations
• Part II Advanced Optimization
• Part III Learning
• Part IV Appendix.

Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator, and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.

• Theory of Evolutionary Computing (I).- Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem.- Mirrored Sampling and Sequential Selection for Evolution Strategies.- Optimisation and Generalisation: Footprints in Instance Space.- Adaptive Drift Analysis.- Optimizing Monotone Functions Can Be Difficult.- Log-Linear Convergence of the Scale-Invariant (?/? w , ?)-ES and Optimal ? for Intermediate Recombination for Large Population Sizes.- Exploiting Overlap When Searching for Robust Optima.- Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.- One-Point Geometric Crossover.- When Does Dependency Modelling Help? Using a Randomized Landscape Generator to Compare Algorithms in Terms of Problem Structure.- First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes.- Differential Mutation Based on Population Covariance Matrix.- General Lower Bounds for the Running Time of Evolutionary Algorithms.- A Binary Encoding Supporting Both Mutation and Recombination.- Towards Analyzing Recombination Operators in Evolutionary Search.- Theory of Evolutionary Computing (II).- Bidirectional Relation between CMA Evolution Strategies and Natural Evolution Strategies.- A Fine-Grained View of GP Locality with Binary Decision Diagrams as Ant Phenotypes.- Drift Analysis with Tail Bounds.- More Effective Crossover Operators for the All-Pairs Shortest Path Problem.- Comparison-Based Adaptive Strategy Selection with Bandits in Differential Evolution.- Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation.- An Archive Maintenance Scheme for Finding Robust Solutions.- Experimental Supplements to the Theoretical Analysis of Migration in the Island Model.- General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms.- Negative Drift in Populations.- Log(?) Modifications for Optimal Parallelism.- The Linkage Tree Genetic Algorithm.- An Analysis of the XOR Dynamic Problem Generator Based on the Dynamical System.- The Role of Degenerate Robustness in the Evolvability of Multi-agent Systems in Dynamic Environments.- Machine Learning, Classifier Systems, Image Processing.- Evolutionary Learning of Technical Trading Rules without Data-Mining Bias.- Using Computational Intelligence to Identify Performance Bottlenecks in a Computer System.- Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation.- Globally Induced Model Trees: An Evolutionary Approach.- Open-Ended Evolutionary Robotics: An Information Theoretic Approach.- A Novel Similarity-Based Crossover for Artificial Neural Network Evolution.- Indirect Encoding of Neural Networks for Scalable Go.- Comparison-Based Optimizers Need Comparison-Based Surrogates.- A Cooperative Coevolutionary Approach to Partitional Clustering.- Feature Selection for Multi-purpose Predictive Models: A Many-Objective Task.- Incorporating Domain Knowledge into Evolutionary Computing for Discovering Gene-Gene Interaction.- The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies.- Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-evolution: Application to Medical 3D Tomography.- Comparative Analysis of Search and Score Metaheuristics for Bayesian Network Structure Learning Using Node Juxtaposition Distributions.- Analyzing the Credit Default Swap Market Using Cartesian Genetic Programming.- Memetic Algorithms, Hybridized Techniques, Meta and Hyperheurisics.- A Memetic Cooperative Optimization Schema and Its Application to the Tool Switching Problem.- Ownership and Trade in Spatial Evolutionary Memetic Games.- A Hyper-Heuristic Approach to Strip Packing Problems.- Asymptotic Analysis of Computational Multi-Agent Systems.- Path-Guided Mutation for Stochastic Pareto Local Search Algorithms.- Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics.- Graph Clustering Based Model Building.- How to Choose Solutions for Local Search in Multiobjective Combinatorial Memetic Algorithms.- Secure and Task Abortion Aware GA-Based Hybrid Metaheuristics for Grid Scheduling.- A Memetic Algorithm for the Pickup and Delivery Problem with Time Windows Using Selective Route Exchange Crossover.- Ant Based Hyper Heuristics with Space Reduction: A Case Study of the p-Median Problem.- A Study of Multi-parent Crossover Operators in a Memetic Algorithm.- A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover.- A Memetic Algorithm with Non Gradient-Based Local Search Assisted by a Meta-model.- Multiobjective Optimization, Theoretical Aspects.- Theoretically Investigating Optimal ?-Distributions for the Hypervolume Indicator: First Results for Three Objectives.- Convergence Rates of (1+1) Evolutionary Multiobjective Optimization Algorithms.- Tight Bounds for the Approximation Ratio of the Hypervolume Indicator.- Evolutionary Multiobjective Optimization Algorithm as a Markov System.- A Natural Evolution Strategy for Multi-objective Optimization.- Solving Multiobjective Optimization Problem by Constraint Optimization.- Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization.- Objective Space Partitioning Using Conflict Information for Many-Objective Optimization.- How Crossover Speeds Up Evolutionary Algorithms for the Multi-criteria All-Pairs-Shortest-Path Problem.- Path Relinking on Many-Objective NK-Landscapes.- In Search of Equitable Solutions Using Multi-objective Evolutionary Algorithms.- Stopping Criteria for Genetic Algorithms with Application to Multiobjective Optimization.- Defining and Optimizing Indicator-Based Diversity Measures in Multiobjective Search.- On Expected-Improvement Criteria for Model-based Multi-objective Optimization.- Parameter Tuning Boosts Performance of Variation Operators in Multiobjective Optimization.
• (source: Nielsen Book Data)

We are very pleased to present to you this LNCS volume, the proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010). PPSN is one of the most respected and highly regarded c- ference series in evolutionary computation, and indeed in natural computation aswell.Thisbiennialeventwas?rstheldinDortmundin1990, andtheninBr- sels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), Granada (2002), Birmingham (2004), Reykjavik (2006) and again in Dortmund in 2008. PPSN 2010 received 232 submissions. After an extensive peer review p- cess involving more than 180 reviewers, the program committee chairs went through all the review reports and ranked the papers according to the revi- ers'comments. Each paper wasevaluated by at least three reviewers.Additional reviewers from the appropriate branches of science were invoked to review into disciplinary papers. The top 128 papers were ?nally selected for inclusion in the proceedings and presentation at the conference. This represents an acceptance rate of 55%, which guarantees that PPSN will continue to be one of the c- ferences of choice for bio-inspired computing and metaheuristics researchers all over the world who value the quality over the size of a conference. The papers included in the proceedingsvolumes covera wide range of topics, fromevolutionarycomputationto swarmintelligence, frombio-inspiredcomp- ing to real-world applications. Machine learning and mathematical games s- portedbyevolutionaryalgorithmsaswellasmemetic, agent-orientedsystemsare also represented. They all are the latest and best in natural computation. The proceedings are composed of two volumes divided into nine thematic sections.
(source: Nielsen Book Data)