https://sci2s.ugr.es/sites/default/files/files/Teaching/GraduatesCourses/Metaheuristicas/Bibliography/VNS.pdf

Variable neighborhood search

Variable Neighborhood Search.

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

/pdf/metaheuristics-from-design-to-implementation-1vqdfv8nk9.pdf

Metaheuristics: From Design to Implementation

On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms

A general model for the coevolution of cooperating species is presented. This model is instantiated and tested in the domain of function optimization, and compared with a traditional GA-based function optimizer. The results are encouraging in two respects. They suggest ways in which the performance of GA and other EA-based optimizers can be improved, and they suggest a new approach to evolving complex structures such as neural networks and rule sets.

/pdf/a-cooperative-coevolutionary-approach-to-function-2aahpt0la3.pdf

A Cooperative Coevolutionary Approach to Function Optimization

A placement algorithm, Genie, is presented for the assignment of modules to locations on chips. Genie is an adaptation of the genetic algorithm technique that has traditionally been a tool of the artificial intelligence community. The technique is a paradigm for examining a state space. It produces its solutions through the simultaneous consideration and manipulation of a set of possible solutions. The manipulations resemble the mechanics of natural evolution. For example, solutions are "mated" to produce "offspring" solutions. Genie has been extensively run on a variety of small test instances. Its solutions were observed to be quite good and in several cases optimal.

http://ieeexplore.ieee.org/iel5/43/28444/01270337.pdf

Genetic Placement

A distributed formulation of the genetic algorithm paradigm is proposed and experimentally analyzed. Our formulation is based in part on two principles of the paleontological theory of punctuated equilibria-allopatric speciation and stasis. Allopatric speciation involves the rapid evolution of new species after being geographically separated. Stasis implies that after equilibria is reached in an environment there is little drift in genetic composition. We applied the formulation to the Optimal Linear Arrangement problem. In our experiments, the result was more than just a hardware acceleration, rather better solutions were obtained with less total work.

Punctuated equilibria: a parallel genetic algorithm

Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried. >

Distributed genetic algorithms for the floorplan design problem

This paper presents a performance-oriented placement and routing tool for field-programmable gate arrays. Using recursive geometric partitioning for simultaneous placement and global routing, and a graph-based strategy for detailed routing, our tool optimizes source-sink pathlengths, channel width and total wire-length. Our results compare favorably with other FPGA layout tools, as measured by the maximum channel width required to place and route a number of industrial benchmarks.

/pdf/performance-oriented-placement-and-routing-for-field-1xrv54o85c.pdf

Performance-oriented placement and routing for field-programmable gate arrays

The island model genetic algorithm shows promise as a superior formulation based on considerations from theories of natural evolution and from the efficiencies of coarsegrained parallel computer architectures. The theory of punctuated equilibria calls for the population to be partitioned into several distinct subpopulations. These subpopulations have extensive periods of isolated evolution, i.e. computation, occasionally interspersed with migration, i.e. communication. It is precisely for this sort of process with its alternating phases of extended computation and limited communication that messagepassing multiprocessors (implementing coarse-grained parallelism) are best suited. We validate this promise of the island model and illustrate the effects of varying configuration attributes through experiments with a difficult very large-scale integration (VLSI) design problem.

James P. Cohoon

Papers

Genetic Placement

Punctuated equilibria: a parallel genetic algorithm

Distributed genetic algorithms for the floorplan design problem

Performance-oriented placement and routing for field-programmable gate arrays

C6.3 Island (migration) models: evolutionary algorithms based on punctuated equilibria