An application of genetic and evolutive algorithms to unit commitment problem

 Here is introduced an application of the Genetic and Evolutive Algorithms to the Unit Commitment Problem. It is a mixed integer problem of constrained non linear combinatorial optimization. The many constraints make the problem very complex. Three cases of study on the problem have been faced, characterized by crescent grades of completeness/ difficulties in order to understand which are the advantages and the difficulties which arise from the evolutive approach. In the cases of study have been faced dimensions of the problem significant in practice: from 10 up to 1000 generators.     

Applying genetic programming technique in classification trees

Classification problems are often encountered in many applications. In the past, classification trees were often generated by decision-tree methods and commonly used to solve classification problems. In this paper, we have proposed an algorithm based on genetic programming to search for an appropriate classification tree according to some criteria. The classification tree obtained can be transferred into a rule set, which can then be fed into a knowledge base to support decision making and facilitate daily operations. Two new genetic operators, elimination and merge, are designed in the proposed approach to remove redundancy and subsumption, thus producing more accurate and concise decision rules than that without using them. Experimental results from the credit card data also show the feasibility of the proposed algorithm.     

Adaptive mutation in genetic algorithms

 In Genetic Algorithms mutation probability is usually assigned a constant value, therefore all chromosome have the same likelihood of mutation irrespective of their fitness. It is shown in this paper that making mutation a function of fitness produces a more efficient search. This function is such that the least significant bits are more likely to be mutated in high-fitness chromosomes, thus improving their accuracy, whereas low-fitness chromosomes have an increased probability of mutation, enhancing their role in the search. In this way, the chance of disrupting a high-fitness chromosome is decreased and the exploratory role of low-fitness chromosomes is best exploited. The implications of this new mutation scheme are assessed with the aid of numerical examples.     

Parallel breadth-first search algorithms for trees and graphs

Parallel Breadth-First Search (BFS) algorithms for ordered trees and graphs on a shared memory model of a Single Instruction-stream Multiple Data-stream computer are proposed. The parallel BFS algorithm for trees computes the BFS rank of eachnode of an ordered tree consisting of n nodes in time of 0(β log n) when 0(n ^1+1/β) processors are used, β being an integer greater than or equal to 2. The parallel BFS algorithm for graphs produces Breadth-First Spanning Trees (BFSTs) of a directedgraph G having n nodes in time 0(log d.log n) using 0(n ³) processors, where d is the diameter of G If G is a strongly connected graph or a connected undirected graph the BFS algorithm produces n BFSTs, each BFST having a different start node.     

Applying genetic algorithms to zone design

Genetic algorithms (GA) have been found to provide global near optimal solutions in a wide range of complex problems. In this paper genetic algorithms have been used to deal with the complex problem of zone design. The zone design problem comprises a large number of geographical tasks, from which electoral districting is probably the most well known. The electoral districting problem is described and formalized mathematically. Different problem encodings, suited to GA optimization, are presented, together with different objective functions. A practical real world example is given and tests performed in order to evaluate the effectiveness of the GA approach.     

A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

 In the present paper a special bit-masking oriented data structure for an improved implementation of crossover and mutation operators in genetic algorithms is shown. The developed data structure performs evolutionary operators in two separate steps: crossover and mutation mask fill and a special boolean based function application. Both phases are optimized to reach a more efficient, fast and flexible genetic reproduction than standard implementations. The method has been powered adding a multi-layered, bit-masking oriented data structure and a boolean operation based control mixer, allowing special blended crossover operators obtained by superposition of the standard ones. Several examples of crossover schemes produced by these extended controls are presented. In addition, a special purpose crossover scheme, capable to process at the same time two distinct groups of design variables with separate crossover schemes is shown, in order to improve efficiency and convergence speed of some discrete/continuous optimization problems. Finally, to highlight further capabilities of the bit-masking approach, a special single-step version of an evolutionary direction operator is also illustrated.     

On coevolutionary genetic algorithms

 The use of evolutionary computing techniques in coevolutionary/multi-agent systems is becoming increasingly popular. This paper presents simple models of the genetic algorithm in such systems, with the aim of examining the effects of different types of interdependence between individuals. Using the model it is shown that, for a fixed amount of interdependence between coevolving individuals, the existence of partner gene variance and the level at which fitness is applied can have significant effects, as does the evaluation partnering strategy used.     

Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning

Unscheduled maintenance of aircraft can cause significant costs. The machine needs to be repaired before it can operate again. Thus it is desirable to have concepts and methods to prevent unscheduled maintenance. This paper proposes a method for forecasting the condition of aircraft air conditioning system based on observed past data. Forecasting is done in a point by point way, by iterating the algorithm. The proposed method uses decision trees to find and learn patterns in past data and use these patterns to select the best forecasting method to forecast future data points. Forecasting a data point is based on selecting the best applicable approximation method. The selection is done by calculating different features/attributes of the time series and then evaluating the decision tree. A genetic algorithm is used to find the best feature set for the given problem to increase the forecasting performance. The experiments show a good forecasting ability even when the function is disturbed by noise.     

The phenomenon of decision oscillation: A new consequence of pathology in game trees

Random minimaxing studies the consequences of using a random number for scoring the leaf nodes of a full width game tree and then computing the best move using the standard minimax procedure. Experiments in Chess showed that the strength of play increases as the depth of the lookahead is increased. Previous research by the authors provided a partial explanation of why random minimaxing can strengthen play by showing that, when one move dominates another move, then the dominating move is more likely to be chosen by minimax. This paper examines a special case of determining the move probability when domination does not occur. Specifically, we show that, under a uniform branching game tree model, whether the probability that one move is chosen rather than another depends not only on the branching factors of the moves involved, but also on whether the number of ply searched is odd or even. This is a new type of game tree pathology, where the minimax procedure will change its mind as to which move is best, independently of the true value of the game, and oscillate between moves as the depth of lookahead alternates between odd and even.     

Efficient algorithms to create and maintain balanced and threaded binary search trees

The algorithm proposed by Chang and lyengar to perfectly balance binary search trees has been modified to not only balance but also thread binary search trees. Threads are constructed in the same sequence as normal pointers during the balancing process. No extra workspace is necessary, and the running time is also linear for the modified algorithm. Such produced tree structure has minimal average path length for fast information retrieval, and threads to facilitate more flexible and efficient traversing schemes. Maintenance and manipulation of the data structure are discussed and relevant algorithms given.     

Operator and parameter adaptation in genetic algorithms

 Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the “population”. Members of the population are usually represented as strings written over some fixed alphabet, each of which has a scalar value attached to it reflecting its quality or “fitness”. The search may be seen as the iterative application of a number of operators, such as selection, recombination and mutation, to the population with the aim of producing progressively fitter individuals. These operators are usually static, that is to say that their mechanisms, parameters, and probability of application are fixed at the beginning and constant throughout the run of the algorithm. However, there is an increasing body of evidence that not only is there no single choice of operators which is optimal for all problems, but that in fact the optimal choice of operators for a given problem will be time-variant i.e. it will depend on such factors as the degree of convergence of the population. Based on theoretical and practical approaches, a number of authors have proposed methods of adaptively controlling one or more of the operators, usually invoking some kind of “meta-learning” algorithm, in order to try and improve the performance of the Genetic Algorithm as a function optimiser. In this paper we describe the background to these approaches, and suggest a framework for their classification, based on the learning strategy used to control them, and what facets of the algorithm are susceptible to adaptation. We then review a number of significant pieces of work within the context of this setting, and draw some conclusions about the relative merits of various approaches and promising directions for future work.     

A review of genetic algorithms applied to training radial basis function networks

The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.     

Integrating adaptive mutations and family competition into genetic algorithms as function optimizer

 In this paper, we propose a robust evolutionary algorithm, called adaptive mutations genetic algorithm, for function optimization problems. Our main contribution is robustly optimizing problems whose number of variables from 2 to 200. In order to have a fair comparison, we propose the criteria for constructing a testing bed and for classifying these problems into different complexity degrees. The proposed approach, based on the family competition and multiple adaptive rules, successfully integrates the decreasing-based Gaussian mutation and self-adaptive Cauchy mutation to balance the exploitation and exploration. It is implemented and applied to widely used test functions and several nonseparable multimodal functions. Experimental results indicate that our approach is more robust than ten evolutionary algorithms.     

The model-based dynamic hand posture identification using genetic algorithm

This paper proposes a new hand posture identification system which applies genetic algorithm to develop an efficient 3D hand-model-fitting method. The 3D hand-model-fitting method consists of (1) finding the closed-form inverse kinematics solution, (2) defining the alignment measure function for the wrist-fitting process, and (3) applying genetic algorithm to develop the dynamic hand posture identification process. In contrast to the conventional computationally intensive hand-model-fitting methods, we develop an off-line training process to find the closed-form inverse kinematics solution functions, and a fast model-based hand posture identification process. In the experiments, we will illustrate that our hand posture identification system is very effective. Received: 10 April 1997 / Accepted: 18 June 1998     

A hybrid approach based on genetic algorithms and (max, +) algebra for network applications

The following work addresses the problem of scheduling operations on a flow network, as well as alignment (path) allocation. This is a multi-objective problem, and this paper proposes a solution method through a hybrid approach based on a genetic algorithm in conjunction with (max, +) algebra. A concise system abstraction is proposed through a non-linear (max, +) model. This model describes the main optimization constraints which dictate the behavior of the mutation and crossover operations in the genetic algorithm. Additionally, each individual in the population represents the value assignment of the decision variables, which linearizes the (max, +) model. A hierarchic genetic structure is proposed for individuals such that variable dependence is modeled. For each individual, the (max, +)-linear model is solved through a matrix product which determines the daters for alignment enabling for transfer operations. The study is extendable to complex net-structured systems of different nature.     

Genetic algorithms with age structure

 This paper deals with genetic algorithms with age structure. Evolutionary optimization methods have been successfully applied to complex optimization problems, but the evolutionary optimization methods have a problem of bias in candidate solutions due to genetic drift in search. To solve this problem, we propose the introduction of age structure into genetic algorithms as a simple extension. In nature, an individual is removed from a population when the individual reaches lethal age. Therefore, genetic algorithms with age structure (ASGA) can maintain the genetic diversity of a population by removing aged individuals from the population. First, we conduct simple simulations of two subpopulations considering the age structure. Next, we apply the ASGA to a kanapsack problem. Finally, we discuss the optimal parameters for the age structure of the ASGA. These simulation results indicate that the ASGA can control selection pressure by aging process and relatively maintain the genetic diversity of a population. Received: 17 February 1997/Accepted: 6 May 1997     

Multiple change-point detection with a genetic algorithm

 A common change-point problem is considered where the population mean of a random variable is suspected of undergoing abrupt changes in course of a time series. It is usual in practice that no information on positions or number of such shifts is available beforehand. Finding the change points, i.e. the positions of the shifts, in such a situation is a delicate statistical problem since any considered sample may actually represent a mixture of two or more populations where values from both sides of a yet unrecognized change point are unconsciously assembled. If this is the case, underlying assumptions of an employed statistical two-sample test are usually violated. Consequently, no definite decision should be based on just one value of the test statistic. Such a value is rather, as a precaution, to be regarded as an only approximate indicator of the quality of a hypothesis about change-point positions. Given these conclusions, it is found imperative to treat the problem of multiple change-point detection as one of global optimization. A cost function is constructed in such a manner that the change-point configuration yielding the global optimum is compliant with statistical-theoretical requirements to the utmost extent. The used advanced optimization tool, a genetic algorithm, is both efficient – as it takes advantage of the information about promising change-point positions encountered in previously investigated trial configurations – and flexible (as it is open to any modification of the change-point configuration at any time). Experiments using numerical simulation confirm adequate performance of the method in an application where a common change-point detection procedure based on Student's two-sample t-test is used to detect an arbitrary number of shifts in the mean of a normally distributed random variable.     

Leveraging saving-based algorithms by master-slave genetic algorithms

Saving-based algorithms are commonly used as inner mechanisms of efficient heuristic construction procedures. We present a general mechanism for enhancing the effectiveness of such heuristics based on a two-level genetic algorithm. The higher-level algorithm searches in the space of possible merge lists which are then used by the lower-level saving-based algorithm to build the solution. We describe the general framework and we illustrate its application to three hard combinatorial problems. Experimental results on three hard combinatorial optimization problems show that the approach is very effective and it enables considerable enhancement of the performance of saving-based algorithms.     

Power system model validation for power quality assessment applications using genetic algorithm

This paper presents an intelligent system for power quality assessment application. This system is used for power system model validation. A genetic algorithm (GA) based system for validating the power system model in capacitor switching studies has been developed. The problem formulation and the proposed solution are illustrated. The feasibility of the developed system for practical applications is demonstrated by evaluation studies.