Developing an Adaptation Process for Real-Coded Genetic Algorithms

The genetic algorithm (GA) is a metaheuristic method which simulates the life cycle and the survival of the fittest in the nature for solving optimization problems. This study aimed to develop enhanced operation by modifying the current GA. This development process includes an adaptation method that contains certain developments and adds a new process to the classic algorithm. Individuals of a population will be trialed to adapt to the current solution of the problem by taking them separately for each generation. With this adaptation method, it is more likely to get better results in a shorter time. Experimental results show that this new process accelerated the algorithm and a certain solution has been reached in fewer generations. In addition, better solutions were achieved, especially for a certain number of generations.     

Shape structural optimization with an interior point nonlinear programming algorithm

The aim of this paper is to study the implementation of an efficient and reliable technique for shape optimization of solids, based on general nonlinear programming algorithms. We also study the practical behaviour for this kind of applications of a quasi-Newton algorithm, based on the Feasible Direction Interior Point Method for nonlinear constrained optimization. The optimal shape of the solid is obtained iteratively. At each iteration, a new shape is generated by B-spline curves and a new mesh is automatically generated. The control point coordinates are given by the design variables. Several illustrative two-dimensional examples are solved in a very efficient way. We conclude that the present approach is simple to formulate and to code and that our optimization algorithm is appropriate for this problem. Received May 12, 1999     

Parallel strong orientation of an undirected graph

A dominating set of an undirected graph G is a set D of nodes such that every node of G either is in D or is adjacent to some node of D. It is shown that the problem of finding a minimum cardinality dominating set is NP-complete for split graphs (a subclass of chordal graphs) and bipartite graphs.     

Heuristics for two-machine no-wait flowshop scheduling with an availability constraint

In this paper we study the two-machine no-wait flowshop problem with an availability constraint. The problem has been shown to be NP-hard, and some heuristics with a worst-case error bound of 2 have been developed for it. We provide two improved heuristics for the problem, and show that each has a worst-case error bound of 5/3.     

Ranked reservoir sampling: an extension to the reservoir sampling algorithm

This paper describes a simple extension to the reservoir sampling algorithm to allow its use with ranked records. Here the fixed‐sized sample must include records in order of rank, but a fair selection must occur to choose the lowest‐ranked records included. The result is produced after a single pass through the records. Copyright © 2007 John Wiley & Sons, Ltd.     

Linear self-stabilizing algorithms for the independent and dominating set problems using an unfair distributed scheduler

This paper presents distributed self-stabilizing algorithms for the maximal independent and the minimal dominating set problems. Using an unfair distributed scheduler the algorithms stabilizes in at most max{3n−5,2n} resp. 9n moves. All previously known algorithms required O(n²) moves.     

Design of an adaptive mutation operator in an electrical load management case study

An adequately designed and parameterized set of operators is crucial for an efficient behaviour of Genetic Algorithms (GAs). Several strategies have been adopted in order to better adapt parameters to the problem under resolution and to increase the algorithm's performance. One of these approaches consists in using operators presenting a dynamic behaviour, that is displaying a different qualitative behaviour in different stages of the evolutionary process. In this work a comparative analysis of the effects of using an adaptive mutation operator is presented in the operational framework of a multi-objective GA for the design and selection of electrical load management strategies. It is shown that the use of a time/space varying mutation operator depending on the values achieved for each objective function increases the performance of the algorithm.     

Distributed evaluation of an iterative function for all object pairs on an SIMD hypercube

An efficient distributed algorithm for evaluating an iterative function on all pairwise combinations of C objects on an SIMD hypercube is presented. The algorithm achieves uniform load distribution and minimal, completely local interprocessor communication.     

Analysis of an adaptive algorithm to find the two nearest neighbors

Given a setS ofN distinct elements in random order and a pivotx ∈S, we study the problem of simultaneously finding the left and the right neighbors ofx, i.e.,L=max{u|u<x} andR=min{v|v>x}. We analyze an adaptive algorithm that solves this problem by scanning the setS while maintaining current values for the neighborsL andR. Each new element inspected is compared first against the neighbor in the most populous side, then (if necessary) against the neighbor in the other side, and finally (if necessary), against the pivot. This algorithm may require 3N comparisons in the worst case, but it performs well on the average. If the pivot has rankαN, where α is fixed and <1/2, the algorithm does (1+α)N+Θ(logN) comparisons on the average, with a variance of 3 lnN+Θ(1). However, in the case where the pivot is the median, the average becomes 3/2;N+Θ(√N), while the variance grows to (1/2−π/8)N+Θ(logN). We also prove that, in the αN case, the limit distribution is Gaussian. This work has been supported in part by Grant FONDECYT(Chile) 1950622 and 1981029. Online publication October 6, 2000.     

数据挖掘及其一种关联规则算法

文章主要论述了数据挖掘的概念、过程及应用前景,此外还重点介绍了数据挖掘中常用的一种算法-关联规则算法.     

Piecemeal Learning of an Unknown Environment

We introduce a new learning problem: learning a graph bypiecemeal search, in which the learner must return every so often to its starting point (for refueling, say). We present two linear-time piecemeal-search algorithms for learningcity-block graphs: grid graphs with rectangular obstacles.     

Casting an Object with a Core

This paper addresses geometric problems in manufacturing objects by casting. In casting, molten material is poured into the cavity of the cast and allowed to solidify, after which the cast is removed. The cast has two cast parts to be removed in opposite directions. To manufacture more complicated objects, the cast may also have a side core to be removed in a direction skewed to the removal directions for the cast parts. We address the following problem: Given an object and the removal directions for the cast parts and the side core, can a cast be constructed such that the cast parts and the side core can be removed in the directions specified without colliding with the object or each other? We give necessary and sufficient conditions for the problem, as well as a discrete algorithm to perform the test in O(n ³log?n) time for polyhedral objects, where n is the number of vertices, edges, and facets. If the test result is positive, a cast with complexity O(n ³) can be constructed within the same time bound. We also present an example to show that a cast may have Ω(n ³) complexity in the worst case.     

Fast non-blocking atomic commit: an inherent trade-off

This paper investigates the time-complexity of the non-blocking atomic commit (NBAC) problem in a synchronous distributed model where t out of n processes may fail by crashing. We exhibit for t?3 an inherent trade-off between the fast abort property of NBAC, i.e., aborting a transaction as soon as possible if some process votes “no”, and the fast commit property, i.e., committing a transaction as soon as possible when all processes vote “yes” and no process crashes. We also give two algorithms: the first satisfies fast commit and a weak variant of fast abort, whereas the second satisfies fast abort and a weak variant of fast commit.     

A linear time algorithm for finding an optimal degree-bounded subtree of an edge-weighted tree

Given an edge-weighted tree T=(V(T),E(T)) and its subtree T^′, for any v∈V(T), the distance d(v,T^′) is defined as the minimum weighted distance from v to any vertex in T^′. The distance d(T,T^′) is defined as the sum of all distances of the form d(v,T^′) where v∈V(T). We give an algorithm to find a T^′ that minimizes d(T,T^′) and for all w∈V(T^′), the degree degT^′(w) of w is not more than a prespecified bound db(w)(0?db(w)?degT(w)) at w. The worst-case running time of our algorithm is in O(|V(T)|).     

Intelligent Control for an Acrobot

The acrobot is an underactuated two-link planar robot that mimics the human acrobat who hangs from a bar and tries to swing up to a perfectly balanced upside-down position with his/her hands still on the bar. In this paper we develop intelligent controllers for swing-up and balancing of the acrobot. In particular, we first develop classical, fuzzy, and adaptive fuzzy controllers to balance the acrobot in its inverted unstable equilibrium region. Next, a proportional-derivative (PD) controller with inner-loop partial feedback linearization, a state-feedback, and a fuzzy controller are developed to swing up the acrobot from its stable equilibrium position to the inverted region, where we use a balancing controller to catch and balance it. At the same time, we develop two genetic algorithms for tuning the balancing and swing-up controllers, and show how these can be used to help optimize the performance of the controllers. Overall, this paper provides (i) a case study of the development of a variety of intelligent controllers for a challenging application, (ii) a comparative analysis of intelligent vs. conventional control methods (including the linear quadratic regulator and feedback linearization) for this application, and (iii) a case study of the development of genetic algorithms for off-line computer-aided-design of both conventional and intelligent control systems.     

Asymptotic analysis of an optimized quicksort algorithm

Jon Bentley and Douglas McIlroy have implemented a fast quicksort for the C standard library in 1993. We consider here the average-case complexity in terms of number of comparisons of this algorithm, and give its asymptotic expansion up to the constant order.     

A running time analysis of an Ant Colony Optimization algorithm for shortest paths in directed acyclic graphs

In this paper, we prove polynomial running time bounds for an Ant Colony Optimization (ACO) algorithm for the single-destination shortest path problem on directed acyclic graphs. More specifically, we show that the expected number of iterations required for an ACO-based algorithm with n ants is for graphs with n nodes and m edges, where ρ is an evaporation rate. This result can be modified to show that an ACO-based algorithm for One-Max with multiple ants converges in expected iterations, where n is the number of variables. This result stands in sharp contrast with that of Neumann and Witt, where a single-ant algorithm is shown to require an exponential running time if ρ=O(n−1−ε) for any ε>0.