Abs t rac t At the heart of many optimizat ion procedures are powerful pruning and propagation rules. This paper presents a case study in the construction of such rules. We develop a new algor i thm, Complete Decreasing Best F i t , that finds the opt imal packing of objects into bins. The algor i thm use a branching rule based on the well known Decreasing Best Fi t approximat ion algor i thm. ...

This paper presents the technique for accelerating 3-Sat isfiability (3-SAT) logic programming in Hopfield neural network. The core impetus for this work is to integrate activation function for doing 3-SAT logic programming in Hopfield neural network as a single hybrid network. In logic programming, the activation function can be used as a dynamic post optimizat ion paradigm to transform the ac...

I. Abstract: The prediction of the optimal weld bead width is an important aspect in shielded metal arc welding (SMAW) process as it is related to the strength of the weld. This paper focuses on investigation of the development of the simple and accurate model for prediction of weld bead width of butt joint of SMAW process. Artificial neural networks technique was used to train a program in C++...

There are various bio inspired and evolutionary approaches including genetic programming (GP), Neural Network, Evolutionary programming (EP) exp loited for routing optimizat ion in MANETs and WSNs. The Swarm Intelligence based algorithmic approaches; Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are more p romising in provid ing loop free, energy-aware, and mult i-path rou...

The paper descr ibes a compo nent-based compu tationa l envir onment for imple menting aircr aft desig n probl ems. The envir onment is condu cive to takin g advanta ge of the paral lelisms inher ent in the probl em and distr ibute the indiv idual disci plines on machi nes most appro priate to their needs while insul ating the developer and the user from the compl exity of the underly ing commu...

A bstract. A formalism recently int roduced [4, 5] uses the met hods of statist ical mech anics to mod el the dynamics of gene t ic algor it hms (GAs) . To be of mor e general int erest this formalism must be able to descr ibe problems ot her th an the tes t cases cons idered in [5]. In this pap er , the technique is applied to th e subset sum prob lem , which is a combinatorial optimizat ion p...

Popul at ion diversity loss is a major obstacle in applying genetic algorithms to optimizat ion problems, which often results in population degeneration and pr emature convergence . The diversity changes caused by t hree nat ural-select ion strategies-comparing new offspring to the leastfit spec imen in t he population , to one of t he parents, and to the most similar specimen in t he pop ulat ...

Th e performance of a mean field theory (MFT) neu ral network technique for finding approximate solutions to optimi zation problems is invest igat ed for the case of the minimum cut graph bisectio n problem, which is NPcomplete. We address the issues of solut ion quality, progr amming complexity, convergence tim es and scala bility. Both standard random gr aphs an d mor e st ruct ured geomet ri...

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