https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

/pdf/differential-evolution-a-survey-of-the-state-of-the-art-3lz840pz2t.pdf

Differential Evolution: A Survey of the State-of-the-Art

Introduction to Optimization The Binary Genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index.

https://cdn.preterhuman.net/texts/science_and_technology/nature_and_biology/Bioinformatics/Practical%20Genetic%20Algorithms%20-%20Randy%20L.%20Haupt,%20Sue%20Ellen%20Haupt.pdf

Practical Genetic Algorithms

Phylogenetic trees have a multitude of applications in biology, epidemiology, conservation and even forensics. However, the inference of phylogenetic trees can be extremely computationally intensive. The computational burden of such analyses becomes even greater when model-based methods are used. Model-based methods have been repeatedly shown to be the most accurate choice for the reconstruction of phylogenetic trees, and thus are an attractive choice despite their high computational demands. Using the Maximum Likelihood (ML) criterion to choose among phylogenetic trees is one commonly used model-based technique. Until recently, software for performing ML analyses of biological sequence data was largely intractable for more vi than about one hundred sequences. Because advances in sequencing technology now make the assembly of datasets consisting of thousands of sequences common, ML search algorithms that are able to quickly and accurately analyze such data must be developed if ML techniques are to remain a viable option in the future. I have developed a fast and accurate algorithm that allows ML phylogenetic searches to be performed on datasets consisting of thousands of sequences. My software uses a genetic algorithm approach, and is named GARLI (Genetic Algorithm for Rapid Likelihood Inference). The speed of this new algorithm results primarily from its novel technique for partial optimization of branch-length parameters following topological rearrangements. Experiments performed with GARLI show that it is able to analyze large datasets in a small fraction of the time required by the previous generation of search algorithms. The program also performs well relative to two other recently introduced fast ML search programs. Large parallel computer clusters have become common at academic institutions in recent years, presenting a new resource to be used for phylogenetic analyses. The P-GARLI algorithm extends the approach of GARLI to allow simultaneous use of many computer processors. The processors may be instructed to work together on a phylogenetic search in either a highly coordinated or largely independent fashion.

Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

/pdf/evolving-neural-networks-through-augmenting-topologies-1jipvhvz1f.pdf

Evolving neural networks through augmenting topologies

Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.

/pdf/a-field-guide-to-genetic-programming-3wkkmsw81n.pdf

A Field Guide to Genetic Programming

Optimization is a pivotal aspect of software design. The techniques treated in this text represent research as elucidated by the leaders in the field. The optimization methods are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search.

New Ideas In Optimization

The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004. The 230 revised full papers and 104 poster papers presented were carefully reviewed and selected from 460 submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; artificial immune systems, biological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering.

Genetic and Evolutionary Computation - GECCO 2004

This is one of the only books to provide a complete and coherent review of the theory of genetic programming (GP). In doing so, it provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.

Foundations of Genetic Programming

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors' perspective, including variations in the algorithm, current and ongoing research, applications and open problems.

Riccardo Poli

Papers

A Field Guide to Genetic Programming

New Ideas In Optimization

Genetic and Evolutionary Computation - GECCO 2004

Foundations of Genetic Programming

Particle Swarm Optimisation