Optimization using EAs

Evolutionary Algorithms (EAs) are a class of algorithms which have been inspired by the biological evolutionary principles. Since their inception in the 1960’s and 1970’s several such algorithms have been devised. Evolutionary Strategies (ES), Evolutionary Programming (EP) and Genetic Algorithms (GAs) are some of the algorithms which  have become very popular for solving various search and optimization problems.

EVOLUTIONARY COMPUTATION LITERATURE

Here are some interesting reports/publications/books from the field of Evolutionary Computation, that is related/unrelated to my research

1. Books on Evolutionary Computing (encompassing all EAs in general):

• Essentials of Metaheuristics (My review)
• Evolutionary Computation: A Unified Approach (You may be interested in my review)
• Bio-Inspired Artificial Intelligence
• Swarm Intelligence: From Natural to Artificial Systems
• Evolutionary Algorithms in Theory and Practice

2. Books on Genetic Algorithms (GA):

• An Introduction to Genetic Algorithms
• Genetic Algorithms in Search, Optimization and Machine Learning
• Genetic Algorithms + Data Structures = Evolution Programs

3. Books on Multi-objective Optimization using EAs

• Multi-Objective Optimization using Evolutionary Algorithms
• Evolutionary Algorithms for Solving Multi-Objective Problems

4. Interesting Publications/Technical reports: (Wherever possible, I have linked to the PDF/PS of the papers)

Following are some papers  I have read partially/fully and have found interesting:

• Selected Applications of Natural Computing
• Multi-Objective Optimization using Differential Evolution: A Survey of the State-of-the-Art
• An Extension of Generalized Differential Evolution for Multi-objective Optimization with Constraints
• DEMO: Differential Evolution for Multi-Objective Optimization
• Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation
• Artificial Evolution for Computer Graphics
• The Electric Sheep Screen-Saver: A Case Study in Aesthetic Evolution
• The use of meta-modeling techniques for optimization under uncertainty
• A Framework for Evolutionary Optimization With Approximate Fitness Functions
• A comprehensive review of nature inspired routing algorithms for fixed telecommunication networks
• An Overview of the Simultaneous Perturbation Method for Efficient Optimization (SPSA, useful for multivariate gradient approximation)
• Estimating Nadir Objective Vector quickly using Evolutionary Approaches
• Nash Genetic Algorithms: Examples and Applications
• Finding Knees in Multi-objective Optimization
• A Parameter-Less Genetic Algorithm
• Fitness Functions in Evolutionary Robotics: A survey and analysis
• A Survey of Optimization by Building and Using Probabilistic Model
• Differential evolution-a simple and efficient adaptive scheme for global optimization
• On the Relations between Search and Evolutionary Algorithms
• Analog Genetic Encoding for the Evolution of Circuits & Networks
• Evolutionary Synthesis of Analog Networks (Ph.D thesis)
• Neuroevolution: From architecture to learning
• Evolution of Adaptive Behavior in Robots by Means of Darwinian Selection
• The Good of the Many outweighs the good of the one: Evolutionary Multi-objective Optimization
• Genetic Algorithms and Neural Networks
• Overview of Evolutionary Robotics (Book Chapter)
• Evolving Artificial Neural Networks
• Variable Length Genomes for Evolutionary Algorithms
• Evolutionary Algorithms for Multi-objective Optimization: Methods & Applications ((Ph.D Thesis)
• Niching methods for Genetic Algorithms (Ph.D Thesis)

4. Course Notes/Lectures, etc:

• Natural Computing Course Webpage (Prof. Dr. Thomas Bäck)
  

CONFERENCES IN THE FIELD OF EVOLUTIONARY COMPUTING

• Genetic and Evolutionary Computation Conference (GECCO) (2012 edition)
• IEEE Congress on Evolutionary Computation (IEEE CEC) (2012 edition)
• Parallel Problem Solving from Nature (PPSN) (2012 edition)
• Foundations of Genetic Algorithms (FOGA) (2011 edition)
• Simulated Evolution and Learning (SEAL)  (2012 edition)
• evostar (2012 edition)
• Evolutionary Multi-criterion Optimization (EMO) (2011 edition) [ Papers on DBLP]

MAJOR JOURNALS

• Evolutionary Computation (MIT ECJ)
• IEEE Transactions on Evolutionary Computation (IEEE TEC)

TEST FUNCTIONS FOR OPTIMIZATION USING  EA’s

• Multi-modal Functions: http://www.it.lut.fi/ip/evo/functions/functions.html
• Test problems and Test data for Multi-objective Optimizers
• Some benchmark functions usually used to demonstrate/test optimization powers of Evolutionary Algorithms
• The chapter titled Artificial Landscapes in Back’s Evolutionary Algorithms in theory and practice has various test functions

GENETIC ALGORITHMS EXPLAINED XKCD STYLE

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