The rapid advances in the field of optimization methods in many pure and applied science pose the difficulty of keeping track of the developments as well as selecting an appropriate technique that best suits the problem in-hand. From a practitioner point of view is rightful to wander "which optimization method is the best for my problem?". Looking at the optimization process as a "system" of intercon- nected parts, in this paper are collected some ideas about how to tackle an optimization problem using a class of tools from evolutionary computations called Genetic Algorithms. Despite the number of optimization techniques available nowadays the author of this paper thinks that Genetic Algorithms still play a central role for their versatility, robustness, theoretical framework and simplicity of use. The paper can be considered a "collection of tips" (from literature and personal experience) for the non-computer-scientist that has to deal with optimization problems both in the science and engineering practice. No original methods or algorithms are proposed.

"We have developed a method for prediction of the hardest crystal structures in a given chemical system. It is based on the evolutionary algorithm USPEX and electronegativity-based hardness model that we have augmented with bond-valence model and graph theory. These extensions enable correct description of the hardness of layered, molecular and low-symmetry crystal structures. Applying this method to C and TiO2, we have (i) obtained a number of low-energy carbon structures with hardness slightly lower than diamond and (ii) proved that TiO2 in any of its possible polymorphs cannot be the hardest oxide, its hardness being below 17 GPa."

"With the aim of producing a stable human-like bipedal gait, a five-link planar walking mechanism is coupled with a central pattern generator (CPG) neural network, consisting of units based on Matsuoka's half-center oscillator model with a firm basis in neurophysiology. As a minimalistic approach to bipedal walking, this type of walking mechanism contains only four actuators, and is lacking feet and ankles. The mechanism is simulated with accurate physics, allowing realistic fitness evaluations for the creation of CPG controllers through evolutionary computation. The oscillatory parameters, internal connectivity structure, and external feedback pathways of the networks are determined through genetic algorithms (GA) optimization. The evolved CPG networks are transferred to a hardware implementation of the mechanism, to test their performance under real-world dynamics. Results confirm that the biologically inspired CPG model is very well suited for controlling legged locomotion, since a diverse manifestation of CPG networks (with and without external feedback) have been observed to succeed during the course of GA evaluations. Observations also imply that while the CPG mechanism is inherently able to sustain a stable gait, the utilization of feedback pathways makes the gait more human-like and is needed to provide a means to adapt to irregularities in the environment."

"After a proof of concept using Dropbox(tm), a free storage and synchronization service, showed that an evolutionary algorithm using several dissimilar computers connected via WiFi or Ethernet had a good scaling behavior in terms of evaluations per second, it remains to be proved whether that effect also translates to the algorithmic performance of the algorithm. In this paper we will check several different, and difficult, problems, and see what effects the automatic load-balancing and asynchrony have on the speed of resolution of problems."

"We investigate the evolutionary dynamics of an idealised model for the robust self-assembly of two-dimensional structures called polyominoes. The model includes rules that encode interactions between sets of square tiles that drive the self-assembly process. The relationship between the model's rule set and its resulting self-assembled structure can be viewed as a genotype-phenotype map and incorporated into a genetic algorithm."

A tiny Genetic Programming Framework for C#

Awesome genetic algorithm for creating vehicles in flash.

discusses "EvolutionChamber"