Genetic Programming -- Santa Fe Trail Hazards -- Size Control with Maximum Homologous Crossover -- Machine Learning -- A New Classification-Rule Pruning Procedure for an Ant Colony Algorithm -- Swarm-Based Distributed Clustering in Peer-to-Peer Systems -- Simultaneous Optimization of Weights and Structure of an RBF Neural Network -- An Exponential Representation in the API Algorithm for Hidden Markov Models Training -- Applications -- Memetic Algorithms for the MinLA Problem -- Niching in Evolution Strategies and Its Application to Laser Pulse Shaping -- A Modified Genetic Algorithm for the Beam Angle Optimization Problem in Intensity-Modulated Radiotherapy Planning -- Combinatorial Optimization -- On a Property Analysis of Representations for Spanning Tree Problems -- A Cooperative Multilevel Tabu Search Algorithm for the Covering Design Problem -- Enhancements of NSGA II and Its Application to the Vehicle Routing Problem with Route Balancing -- The Importance of Scalability When Comparing Dynamic Weighted Aggregation and Pareto Front Techniques -- Co-evolution -- A Backbone-Based Co-evolutionary Heuristic for Partial MAX-SAT -- Analysing Co-evolution Among Artificial 3D Creatures -- Self-assembling -- A Critical View of the Evolutionary Design of Self-assembling Systems -- Algorithmic Self-assembly by Accretion and by Carving in MGS -- Evolutionary Design of a DDPD Model of Ligation -- Artificial Life and Bioinformatics -- Population Structure and Artificial Evolution -- Outlines of Artificial Life: A Brief History of Evolutionary Individual Based Models -- An Enhanced Genetic Algorithm for Protein Structure Prediction Using the 2D Hydrophobic-Polar Model -- Incorporating Knowledge of Secondary Structures in a L-System-Based Encoding for Protein Folding -- Advances -- The Electromagnetism Meta-heuristic Applied to the Resource-Constrained Project Scheduling Problem -- Applications of Racing Algorithms: An Industrial Perspective -- An Immunological Algorithm for Global Numerical Optimization -- Algorithms (X, sigma, eta): Quasi-random Mutations for Evolution Strategies.