With regard to literature, improved estimations for the number of possible moves and placements are provided, showing that the complexity of Diplomacy is enormous, making it a good candidate for machine learning and evolutionary learning techniques. To enhance the playing strength of an existing Diplomacy bot and alleviate the distance to the presumed best current bot, a look-ahead planning com...

The learning automata operate in unknown random environments and progressively improve their performance via a learning process. The learning automata are very useful for optimization of multi-modal functions when the function is unknown and only noise-corrupted evaluations are available. In this paper we propose a new hybrid algorithm for noisy optimization. This model is obtained by combining...

__________________________________________________________________________ A recently developed radii-based evolutionary algorithm designed to solve multimodal optimization problems is presented. The approach can be placed within the genetic chromodynamics framework. The basic motivation for modifying the original algorithm was to preserve its ability to search for many optima in parallel while...

In the field of machine learning, data set features such as across-class imbalance and class overlap often pose difficulties for classifier algorithms. A number of methods alleviate these difficulties by adjusting the distribution of the data set before classifier construction. Resampling is typically effected by re-weighting, removing, or duplicating instances. Finding a good distribution for ...

The Learnable Evolution Model (LEM), first presented at the Fourth International Workshop on Multistrategy Learning, employs machine learing to guide evolutionary computation . Specifically, LEM integrates two modes of operation: Machine Learning mode, which employs a machine learning algorithm, and Darwinian Evolution mode, which employs a conventional evolutionary algorithm. The central new i...

The combination of evolutionary algorithms and ANN has been a recent interest in the field of research. Hopfield model is a type of recurrent neural network which has been widely studied for the purpose of associative memories. In the present work, this Hopfield Model of feedback neural networks has been studied with Monte Carlo adaptation learning rule and one evolutionary searching algorithm ...

Optimizing the database queries is one of hard research problems. Exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. The use of evolutionary methods, beca...

In literature, Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. Previous studies focus on approximation algorithms which search for optimal solution in relatively large space. Thus, these consume a amount of computational resources while the quality obtained results lower than expected. order to enhance performance process, this paper proposes two different approaches are in...

Active composites consisting of materials that respond differently to environmental stimuli can transform their shapes. Integrating active and 4D printing allows the printed structure have a pre-designed complex material or property distribution on numerous small voxels, offering enormous design flexibility. However, this tremendous space also poses challenge in efficiently finding appropriate ...

through the last three decades different seismological and engineering approaches for the generation of artificial earthquakes have been proposed. selection of an appropriate method for the generation of applicable artificial earthquake accelerograms (aeas) has been a challenging subject in the time history analysis of the structures in the case of the absence of sufficient recorded accelerogra...