An Evolutionary Algorithm for the Estimation of Threshold Vector Error Correction Models

We develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since disregarding a threshold in cointegration models renders standard approaches to the estimation of the cointegration vectors inefficient, TVECM necessitate a simultaneous estimation of the cointegration vector(s) and the threshold. As far as two cointegrated variables are considered this is commonly achieved by a grid search. However, grid search quickly becomes computationally unfeasible if more than two variables are cointegrated. Therefore, the likelihood function has to be maximized using heuristic approaches. Depending on the precise problem structure the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.evolutionary strategy, genetic algorithm, TVECM

A multidirectional modified Physarum solver for discrete decision making

In this paper, a bio-inspired algorithm able to incrementally grow decision graphs in multiple directions is presented. The heuristic draws inspiration from the behaviour of the slime mould Physarum Polycephalum. In its main vegetative state, the plasmodium, this large single-celled amoeboid organism extends and optimizes a net of veins looking for food. The algorithm is here used to solve classical problems in operations research (symmetric Traveling Salesman and Vehicle Routing Problems). Simulations on selected test cases demonstrate that a multidirectional modied Physarum solver performs better than a unidirectional one. The ability to evaluate decisions from multiple directions enhances the performance of the solver in the construction and selection of optimal decision sequences

An evolutionary algorithm to enhance multivariate Post-Randomization Method (PRAM) protections

The amount of public statistical information available is growing and more accurate protection methods are needed in order to achieve data confidentiality. The Post-Randomization Method (PRAM) protection method was introduced in 1997 as a very powerful method for categorical microdata, but it is still not widely used. This method has a Markov matrix as a parameter. The main problem of the application of this method is that it is difficult to find a good Markov matrix that performs changes in the microdata file producing low loss of valuable information and low risk of disclosure of sensitive data. In this paper we present a methodology that helps us to find a matrix to perform better protections. This is achieved by using an evolutionary algorithm with integrated Information Loss and Disclosure Risk measures. Experiments using three different datasets are also presented in order to empirically evaluate the application of this technique. © 2014 Elsevier Inc. All rights reserved.This work has been done under the PhD in Computer Science program of the Universitat Autònoma de Barcelona (UAB). It is also partially supported by the Spanish MEC ARES-CONSOLIDER INGENIO 2010 CSD2007-00004, and COPRIVACY TIN2011-27076-C03-03. The research leading to these results has also received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Num. 262608.Peer Reviewe

Parallelization of Ant System for GPU under the PRAM Model

We study the parallelized ant system algorithm solving the traveling salesman problem on n cities. First, following the series of recent results for the graphics processing unit, we show that they translate to the PRAM (parallel random access machine) model. In addition, we develop a novel pheromone matrix update method under the PRAM CREW (concurrent-read exclusive-write) model and translate it to the graphics processing unit without atomic instructions. As a consequence, we give new asymptotic bounds for the parallel ant system, resulting in step complexities O(n łg łg n) on CRCW (concurrent-read concurrent-write) and O(n łg n) on CREW variants of PRAM using n2 processors in both cases. Finally, we present an experimental comparison with the currently known pheromone matrix update methods on the graphics processing unit and obtain encouraging results

An Evolutionary Optimization Approach for Categorical Data Protection

The continuous growing amount of public sensible data has increased the risk of breaking the privacy of people or institutions in those datasets. Many protection methods have been developed to solve this problem by either distorting or generalizing data but taking into account the difficult tradeoff between data utility (information loss) and protection against disclosure (disclosure risk). In this paper we present an optimization approach for data protection based on an evolutionary algorithm which is guided by a combination of information loss and disclosure risk measures. In this way, state-of-the-art protection methods are combined to obtain new data protections with a better trade-off between these two measures. The paper presents several experimental results that assess the performance of our approach

Root Shoot Coordination Optimization: Conceptualizing Ascent of Sap and Translocation of Solute in Plant

A new nature inspired evolutionary technique called Root Shoot Coordination Optimization (RSCO) has been proposed here. This optimization method has been developed on the basis of conduction procedure of plant. Water and solute i.e. food circulation phenomena maximizes on the fruitful coordination between root and leaves/shoot. This circulation procedure in plant incorporates two vital processes which are ascent of sap and translocation of food. Ascent of sap occurs due to the combined effect of adhesion and cohesion tension of water molecules and transpiration pull for the evaporation of water through stomata of shoot. Translocation of food takes place due to the pressure difference of solute in the shoot and root. This thought has been mathematically modeled as a new soft computing tool. This method has been tested for some benchmark problems. This method showed its effectiveness with encouraging results

Algorithm and Hardware Co-design for Learning On-a-chip

abstract: Machine learning technology has made a lot of incredible achievements in recent years. It has rivalled or exceeded human performance in many intellectual tasks including image recognition, face detection and the Go game. Many machine learning algorithms require huge amount of computation such as in multiplication of large matrices. As silicon technology has scaled to sub-14nm regime, simply scaling down the device cannot provide enough speed-up any more. New device technologies and system architectures are needed to improve the computing capacity. Designing specific hardware for machine learning is highly in demand. Efforts need to be made on a joint design and optimization of both hardware and algorithm. For machine learning acceleration, traditional SRAM and DRAM based system suffer from low capacity, high latency, and high standby power. Instead, emerging memories, such as Phase Change Random Access Memory (PRAM), Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM), and Resistive Random Access Memory (RRAM), are promising candidates providing low standby power, high data density, fast access and excellent scalability. This dissertation proposes a hierarchical memory modeling framework and models PRAM and STT-MRAM in four different levels of abstraction. With the proposed models, various simulations are conducted to investigate the performance, optimization, variability, reliability, and scalability. Emerging memory devices such as RRAM can work as a 2-D crosspoint array to speed up the multiplication and accumulation in machine learning algorithms. This dissertation proposes a new parallel programming scheme to achieve in-memory learning with RRAM crosspoint array. The programming circuitry is designed and simulated in TSMC 65nm technology showing 900X speedup for the dictionary learning task compared to the CPU performance. From the algorithm perspective, inspired by the high accuracy and low power of the brain, this dissertation proposes a bio-plausible feedforward inhibition spiking neural network with Spike-Rate-Dependent-Plasticity (SRDP) learning rule. It achieves more than 95% accuracy on the MNIST dataset, which is comparable to the sparse coding algorithm, but requires far fewer number of computations. The role of inhibition in this network is systematically studied and shown to improve the hardware efficiency in learning.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

An adaptive parallel genetic algorithm.

Chi Wai Ho, Raymond.Thesis submitted in: December 1999.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 93-97).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.7Chapter 1.1 --- Thesis Outline --- p.10Chapter 1.2 --- Contribution at a Glance --- p.11Chapter Chapter 2 --- Background Concept and Related Work --- p.14Chapter 2.1 --- Genetic Algorithms (GAs) --- p.14Chapter 2.2 --- The Nature of GAs --- p.16Chapter 2.3 --- The Role of Mutation --- p.17Chapter 2.4 --- The Role of Crossover --- p.18Chapter 2.5 --- The Roles of the Mutation and Crossover Rates --- p.19Chapter 2.6 --- Adaptation of the Mutation and Crossover Rates --- p.19Chapter 2.7 --- Diversity Control --- p.21Chapter 2.8 --- Coarse-grain Parallel Genetic Algorithms --- p.25Chapter 2.9 --- Adaptation of Migration Period --- p.26Chapter 2.10 --- Serial and Parallel GAs --- p.27Chapter 2.11 --- Distributed Java Machine (DJM) --- p.28Chapter 2.12 --- Clustering --- p.30Chapter Chapter 3 --- Adaptation of the Mutation and Crossover Rates --- p.35Chapter 3.1 --- The Probabilistic Rule-based Adaptive Model (PRAM) --- p.35Chapter 3.2 --- Time Complexity --- p.37Chapter 3.3 --- Storage Complexity --- p.38Chapter Chapter 4 --- Diversity Control --- p.39Chapter 4.1 --- Repelling --- p.39Chapter 4.2 --- Implementation --- p.42Chapter 4.3 --- Lazy Repelling --- p.43Chapter 4.4 --- Repelling and Lazy Repelling with Deterministic Crowding --- p.43Chapter 4.5 --- Comparison of Repelling and Lazy Repelling with Recent Diversity Maintenance Models in Time Complexity --- p.44Chapter Chapter 5 --- An Adaptive Parallel Genetic Algorithm --- p.46Chapter 5.1 --- A Steady-State Genetic Algorithm --- p.46Chapter 5.2 --- An Adaptive Parallel Genetic Algorithm (aPGA) --- p.47Chapter 5.3 --- An Adaptive Parallel Genetic Algorithm for Clustering --- p.48Chapter 5.4 --- Implementation --- p.48Chapter 5.5 --- Time Complexity --- p.51Chapter Chapter 6 --- Performance Evaluation of PRAM --- p.52Chapter 6.1 --- Solution Quality --- p.58Chapter 6.2 --- Efficiency --- p.60Chapter 6.3 --- Discussion --- p.62Chapter Chapter 7 --- Performance Evaluation of Repelling --- p.66Chapter 7.1 --- Performance Comparison of Repelling and Lazy Repelling with Deterministic Crowding --- p.70Chapter 7.2 --- Performance Comparison with Recent Diversity Maintenance Models --- p.73Chapter 7.3 --- Performance Comparison with Serial and Parallel Gas --- p.75Chapter Chapter 8 --- Performance Evaluation of aPGA --- p.78Chapter 8.1 --- Scalability of Different Dimensionalities --- p.78Chapter 8.2 --- Speedup of Schwefel's function --- p.83Chapter 8.3 --- Solution Quality of Clustering Problems --- p.87Chapter 8.4 --- Speedup of The Clustering Problem --- p.89Chapter Chapter 9 --- Conclusion --- p.9