Mathematica--A System for Doing Mathematics by Computer.

Graphical abstractDisplay Omitted HighlightsProvide an updated and systematic review of distributed evolutionary algorithms.Classify the models into population and dimension-distributed groups semantically.Analyze the parallelism, search behaviors, communication costs, scalability, etc.Highlight recent research hotspots in this field.Discuss challenges and potential research directions in this field. The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.

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Distributed evolutionary algorithms and their models

This paper describes two elitism-based compact genetic algorithms (cGAs)-persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient cGAs by treating them as estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation costs. The idea is to deal with issues connected with lack of memory by allowing a selection pressure that is high enough to offset the disruptive effect of uniform crossover. The pe-cGA finds a near optimal solution (i.e., a winner) that is maintained as long as other solutions generated from probability vectors are no better. The ne-cGA further improves the performance of the pe-cGA by avoiding strong elitism that may lead to premature convergence. It also maintains genetic diversity. This paper also proposes an analytic model for investigating convergence enhancement.

Elitism-based compact genetic algorithms

Friction stir welding (FSW), a highly efficient solid-state joining technique, has been termed as “green” technology due to its energy efficiency and environment friendliness. It is an enabling technology for joining metallic materials, in particular lightweight high-strength aluminum and magnesium alloys which were classified as unweldable by traditional fusion welding. It is thus considered to be the most significant development in the area of material joining over the past two decades. Friction stir processing (FSP) was later developed based on the basic principles of FSW. FSP has been proven to be an effective and versatile metal-working technique for modifying and fabricating metallic materials. FSW/FSP of aluminum alloys has prompted considerable scientific and technological interest since it has a potential for revolutionizing the manufacturing process in the aerospace, defense, marine, automotive, and railway industries. To promote widespread applications of FSW/FSP technology and ensure t...

Recent Advances in Friction Stir Welding/Processing of Aluminum Alloys: Microstructural Evolution and Mechanical Properties

Digital Hardware Evolution.- An Online EHW Pattern Recognition System Applied to Sonar Spectrum Classification.- Design of Electronic Circuits Using a Divide-and-Conquer Approach.- Implementing Multi-VRC Cores to Evolve Combinational Logic Circuits in Parallel.- An Intrinsic Evolvable Hardware Based on Multiplexer Module Array.- Estimating Array Connectivity and Applying Multi-output Node Structure in Evolutionary Design of Digital Circuits.- Research on the Online Evaluation Approach for the Digital Evolvable Hardware.- Research on Multi-objective On-Line Evolution Technology of Digital Circuit Based on FPGA Model.- Evolutionary Design of Generic Combinational Multipliers Using Development.- Analog Hardware Evolution.- Automatic Synthesis of Practical Passive Filters Using Clonal Selection Principle-Based Gene Expression Programming.- Research on Fault-Tolerance of Analog Circuits Based on Evolvable Hardware.- Analog Circuit Evolution Based on FPTA-2.- Bio-inspired Systems.- Knowledge Network Management System with Medicine Self Repairing Strategy.- Design of a Cell in Embryonic Systems with Improved Efficiency and Fault-Tolerance.- Design on Operator-Based Reconfigurable Hardware Architecture and Cell Circuit.- Bio-inspired Systems with Self-developing Mechanisms.- Development of a Tiny Computer-Assisted Wireless EEG Biofeedback System.- Steps Forward to Evolve Bio-inspired Embryonic Cell-Based Electronic Systems.- Evolution of Polymorphic Self-checking Circuits.- Mechanical Hardware Evolution.- Sliding Algorithm for Reconfigurable Arrays of Processors.- System-Level Modeling and Multi-objective Evolutionary Design of Pipelined FFT Processors for Wireless OFDM Receivers.- Reducing the Area on a Chip Using a Bank of Evolved Filters.- Evolutionary Design.- Walsh Function Systems: The Bisectional Evolutional Generation Pattern.- Extrinsic Evolvable Hardware on the RISA Architecture.- Evolving and Analysing "Useful" Redundant Logic.- Adaptive Transmission Technique in Underwater Acoustic Wireless Communication.- Autonomous Robot Path Planning Based on Swarm Intelligence and Stream Functions.- Research on Adaptive System of the BTT-45 Air-to-Air Missile Based on Multilevel Hierarchical Intelligent Controller.- The Design of an Evolvable On-Board Computer.- Evolutionary Algorithms in Hardware Design.- Extending Artificial Development: Exploiting Environmental Information for the Achievement of Phenotypic Plasticity.- UDT-Based Multi-objective Evolutionary Design of Passive Power Filters of a Hybrid Power Filter System.- Designing Electronic Circuits by Means of Gene Expression Programming II.- Designing Polymorphic Circuits with Evolutionary Algorithm Based on Weighted Sum Method.- Robust and Efficient Multi-objective Automatic Adjustment for Optical Axes in Laser Systems Using Stochastic Binary Search Algorithm.- Minimization of the Redundant Sensor Nodes in Dense Wireless Sensor Networks.- Evolving in Extended Hamming Distance Space: Hierarchical Mutation Strategy and Local Learning Principle for EHW.- Hardware Implementation of Evolutionary Algorithms.- Adaptive and Evolvable Analog Electronics for Space Applications.- Improving Flexibility in On-Line Evolvable Systems by Reconfigurable Computing.- Evolutionary Design of Resilient Substitution Boxes: From Coding to Hardware Implementation.- A Sophisticated Architecture for Evolutionary Multiobjective Optimization Utilizing High Performance DSP.- FPGA-Based Genetic Algorithm Kernel Design.- Using Systolic Technique to Accelerate an EHW Engine for Lossless Image Compression.

Evolvable systems : from biology to hardware : 7th International Conference, ICES 2007, Wuhan, China, September 21-23, 2007 : proceedings

The combination of genetic and local search heuristics has been shown to be an effective approach to solving the traveling salesman problem (TSP). This paper describes a new hybrid algorithm that exploits a compact genetic algorithm in order to generate high-quality tours, which are then refined by means of the Lin-Kernighan (LK) local search. The local optima found by the LK local search are in turn exploited by the evolutionary part of the algorithm in order to improve the quality of its simulated population. The results of several experiments conducted on different TSP instances with up to 13,509 cities show the efficacy of the symbiosis between the two heuristics.

A hybrid heuristic for the traveling salesman problem

Hardware-software partitioning is one of the most important issues of codesign of embedded systems because it is made at the beginning of the cycle of design. In terms of costs and delays, final results will strongly depend on partitioning. In this work we address the functional partitioning problem of hardware-software codesign using a genetic algorithm. Experimental results includes a comparative study with three algorithms: simulated annealing, Fiduccia-Matheyses and a modified version of this with an improvement of results.

Functional partitioning for hardware-software codesign using genetic algorithms

In deep submicron circuits, high temperatures have created critical issues in reliability, timing, performance, coolings costs and leakage power. Task migration techniques have been proposed to manage efficiently the thermal distribution in multi-processor systems but at the cost of important performance penalties. While traditional techniques have focused on reducing the average temperature of the chip, they have not considered the effect that temperature gradients have in system reliability. In this work, we explore the benefits of thermal-aware task migration techniques for embedded multi-processor systems. We propose several policies that are able to reduce the average temperature of the chip and the thermal gradients with a negligible performance overhead. With our techniques, hot spots and temperature gradients are decreased up to 30% with respect to state-of-the-art thermal management approaches.

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Adaptive Task Migration Policies for Thermal Control in MPSoCs

http://digital.csic.es/bitstream/10261/111305/4/Analysis%20unstressedlattice.pdf

Analysis of the unstressed lattice spacing, d0, for the determination of the residual stress in a friction stir welded plate of an age-hardenable aluminum alloy - Use of equilibrium conditions and a genetic algorithm

This paper deals with the optimization of parameters of technical indicators for stock market investment. Price prediction is a problem of great complexity and, usually, some technical indicators are used to predict market trends. The main difficulty in using technical indicators lies in deciding a set of parameter values. We proposed the use of Multi-Objective Evolutionary Algorithms (MOEAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. The experimental results indicate that our MOEA offers a solution to the problem by obtaining results that improve those obtained through technical indicators with standard parameters. In order to reduce execution time is necessary to parallelize the executions. Parallelization results show that distributing the workload of indicators in multiple processors to improve performance is recommended. This parallelization has been performed taking advantage of the idle time in a corporate technology infrastructure. We have configured a small parallel grid using the students Labs of a Computer Science University College.

José Ignacio Hidalgo

Papers

A hybrid heuristic for the traveling salesman problem

Functional partitioning for hardware-software codesign using genetic algorithms

Adaptive Task Migration Policies for Thermal Control in MPSoCs

Analysis of the unstressed lattice spacing, d0, for the determination of the residual stress in a friction stir welded plate of an age-hardenable aluminum alloy - Use of equilibrium conditions and a genetic algorithm

A parallel evolutionary algorithm for technical market indicators optimization