WO 2011061548 looks like some bullsh*t.

"This is the first internationally published paper (it was previously published in a Spanish conference of a series that deals with a system, intended for volunteer computing, that uses a pool for implementing distributed evolutionary algorithms. The basic idea is that the population resides in a pool (implemented using CouchDB), with clients pulling individuals from the pool, doing stuff on them, and putting them back in the pool. The algorithm uses, as much as possible, CouchDB features (such as revisions and views) to achieve good performance. All the code (for this and, right now, for the next papers) is available as open-source code."

"This short paper introduces a new way by which to design production system rules. An indirect encoding scheme is presented which views such rules as protein complexes produced by the temporal behaviour of an artificial genetic regulatory network. This initial study begins by using a simple Boolean regulatory network to produce traditional ternary-encoded rules before moving to a fuzzy variant to produce real-valued rules. Competitive performance is shown with related genetic regulatory networks and rule-based systems on benchmark problems."

"We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the Graph Partitioning Problem, which makes use of KaFFPa (Karlsruhe Fast Flow Partitioner). The use of our multilevel graph partitioner KaFFPa provides new effective crossover and mutation operators. By combining these with a scalable communication protocol we obtain a system that is able to improve the best known partitioning results for many inputs in a very short amount of time. For example, in Walshaw's well known benchmark tables we are able to improve or recompute 76% of entries for the tables with 1%, 3% and 5% imbalance."

"This page provides information on the use and implementation of novelty search, an evolutionary search method that takes the radical step of ignoring the objective of search and instead rewarding only behavioral novelty. This visual demonstration (requires modern browser, IE users may need to install a plugin) contrasts a search for novelty with a search for the objective."

The technology has been developed to withstand the challenges of real world — in addition to handling problems of too much data, too little data, correlated data, or noisy data, DataModeler respects the cost and timeliness issues associated with modeling development.

"To define oscillatory movements of securities market, we put in the non-local extension of Ito- equation for wavelet-images of random processes. It is proposed an algorithm of creation of evolutionary equation and a model of prediction of the most probable price movement path. It is carried out experimental validation of findings."

"We have shown that there exists a large ensemble of minimal Boolean networks that show reliable and robust dynamics. The networks are minimal in the respect that the number of connections of a node is not larger than necessary for obtaining a desired reliable trajectory. A reliable trajectory is an attractor of the dynamics of the network that does not change when the update schedule is changed or randomized. This means that under parallel update, at each time step only one node changes its state. The reliable trajectories were chosen at random, given a fixed average number of flips per node. High robustness was achieved by using an evolutionary algorithm that modifies the update functions and that accepts only those changes that do not decrease robustness.…"

"Dynamics of evolutionary games strongly depend on underlying networks. We study the coevolutionary prisoner's dilemma in which players change their local networks as well as strategies (i.e., cooperate or defect). This topic has been increasingly explored by many researchers. On the basis of active linking dynamics [J. M. Pacheco et al., J. Theor. Biol. 243, 437 (2006), J. M. Pacheco et al., Phys. Rev. Lett. 97, 258103 (2006)], we show that cooperation is enhanced fairly robustly. In particular, cooperation evolves when the payoff of the player is normalized by the number of neighbors; this is not the case in the evolutionary prisoner's dilemma on static networks."

"In this paper, a simple Neural controller has been used to achieve stable walking in a NAO biped robot, with 22 degrees of freedom that implemented in a virtual physics-based simulation environment of Robocup soccer simulation environment. The algorithm uses a Matsuoka base neural oscillator to generate control signal for the biped robot. To find the best angular trajectory and optimize network parameters, a new population-based search algorithm, called the Harmony Search (HS) algorithm, has been used. The algorithm conceptualized a group of musicians together trying to search for better state of harmony. Simulation results demonstrate that the modification of the step period and the walking motion due to the sensory feedback signals improves the stability of the walking motion."

"This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a "curiosity instinct", favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct", as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach."

"Each combination of values was then fed into a simulator to give an idea of the grid's performance and its expected lifetime. If the performance was promising, the "genetic material" was subjected to further random changes, or mutation, and this process was repeated until no more improvements were forthcoming.

After 100 generations, the GA spawned a geometry/voltage set that boosted the ion engine grid's lifetime to 5.1 years - at least in the simulator (Journal of Propulsion and Power, DOI: 10.2514/1.44358). Factors optimised included grid hole diameter, hole spacing and the thickness of the grids. The engine could be improved further, says Farnell, by evolving the other parts too.…"

"The results obtained show that there is a clear computational potential within the model; it should therefore be possible to use other similar models as basis for EC techniques.

The adaptation of such models to EC is not straightforward. As these are mostly complex systems, a thorough comprehension of their exact dynamics is often not possible. The choice of how to encode inputs and outputs is also not a simple issue, and can greatly influence their computational potential."

"Pay attention to the path the steepest descent takes until finding the optimum at x = 1 and y = 1. It is said that an evolution strategy would follow a similar path if its population could be infinite. Since, so far, there are no real computers with infinite memory, such assumption cannot be verified in the real world."

"Parallel Global Optimization Algorithms (PGOA) provide an efficient way of dealing with hard optimization problems. One method of parallelization of GOAs that is frequently applied and commonly found in the contemporary literature is the so-called Island Model (IM). In this paper we analyze the impact of the migration topology on the performance of a PGOA which uses the Island Model. In particular we consider parallel Differential Evolution and Simulated Annealing with Adaptive Neighborhood and draw first conclusions that emerge from the conducted experiments."

"The three-dimensional solar structure could, at least in principle, absorb a lot more light and generate more power than a flat panel containing the same area footprint. The hope is that all unused light which has been reflected off one panel would be captured by other panels. Panels of this type would be most ideal in circumstances with limited space."

"This article describes the R package DEoptim which implements the differential evolution algorithm for the global optimization of a real-valued function of a real-valued parameter vector. The implementation of differential evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated via case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-Switching Generalized AutoRegressive Conditional Heteroskedasticity (MSGARCH) model for the returns of the Swiss Market Index."

via: Genetic Argonaut

"To investigate the optimal shape a 3-D panel might take in order to harvest the most light, scientists used a "genetic algorithm" to evolve solar panels in a computer simulation.

The model they developed randomly generated jumbles of flat, triangular double-sided solar panels and analyzed which generated the most power as a virtual sun moved across the sky. The best ones were then "mated" together for "offspring" that combined features of each with "mutations" that varied their structures. This process was then repeated for up to millions of generations, all in order to see what might evolve."