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This paper is the results of research about the weather forecast in Bandung Regency using one of the Evolutionary Algorithms (EA), that is Genetic Programming (GP). In this research, we use the monthly rainfall data in Bandung Regency for the last 11 years (2005–2015). First of all, the data is processed by Weighted Moving Average (WMA) algorithm as preprocessing step. Next, GP Algorithm is used to...
The problem of community detection in complex networks is of high interest in many application domains including sociology, biology, mathematics and economy. Given a set of nodes and links between them, the aim of the problem is to find a grouping of nodes such that a strong community has dense intra-connections and sparse outside community links. In this paper, a coarse-grained evolutionary algorithm...
Feature selection is an extremely important matter in pattern recognition, particularly when a large set of features is available without knowledge about the discriminative information provided by each element. The key issue is to define a criterion in order to rank the features, discarding those features that are less relevant, redundant, or noisy. This depends on the particular task, the classifier...
Procedural Content Generation (PCG) can be a useful tool for aiding creativity in the process of designing game levels. Mixed-initiative level generation tools where a designer and an algorithm collaborate to iteratively generate game levels have been used for this purpose. However, it can be difficult for designers to work with tools that do not respond to the common language of games: game design...
Self-adaptation is an efficient way to control the strategy parameters of an Evolutionary Algorithm automatically during optimization. It is based on implicit evolutionary search in the space of strategy parameters, and has been proven to work well as on-line parameter control method for a variety of strategy parameters, from local to global ones. Our proposed Self-Adaptive Multi-Objective Evolutionary...
The grid is hierarchical in three parts whose functions are quite different. First, the transmission system's role is to transport energy into high voltage from production centers to consumption areas and distribution network directly supplies large industrial consumers and supplying the average and low voltage consumers, with the latest advances in industry and Technological the need for electricity...
This paper concerns evolutionary algorithms for minimization exclusive-or sum-of-products representations of Boolean functions. These representations are used in logic synthesis for certain class of circuits. Minimization is based on a decomposition for Boolean functions with parameter function. Selection of this function is a search task which can be solved with evolutionary algorithms. Algorithms...
As one of the most popular and successful methods in industrial applications, model predictive control (MPC) has attracted increasing interest in the past two decades. However, one of open issues in this research filed is how to solve the constrained nonlinear optimization problems in MPC. From the perspective of evolutionary algorithm, this paper presents a novel population extremal optimization...
Automatic generation of a time table with multi-level constraints is a challenging exercise. Typically, a timetable problem has many possible solutions in the initial search space, each with a distinct fitness level. In this paper we propose a multi-stage hybrid solution based on an evolutionary algorithm where the initial population is first generated that satisfies all the hard constraints. A user...
Software testing is one of the primal phase in various software development lifecycle models and consumes approximately 70% of development time and 40% cost of the overall budget. Nowadays automated testing tools along with different meta-heuristic algorithms which work similarly as simple testing techniques but they significantly outperforms when the complexity of the program is high are used in...
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces...
An approach for robustness analysis of non-dominated solutions to a multi-objective optimization model of an energy management system aggregator (EMSA) in face of uncertainty is presented. The EMSA is an intermediary entity between households and the System Operator (SO), capable of contributing to balance load and supply, and therefore coping with the intermittency of renewable energy sources (RES)...
There are many Evolutionary Algorithms which main features are: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The proposal uses probability density function according to best of initial population to sample new population and save better individuals iteratively. Then using centroid...
Online shopping has become an essential part of our life, which provides a suitable, cheap, and quick way for customers to enjoy a wide variety of products. However, due to the large number of online stores, a customer usually faces difficulties to review all available offers manually in order to find a favorite item. The Internet shopping optimization problem (ISOP) is a multiple-item multiple-shop...
Evolutionary algorithms are popularly used methods to estimate yield for faster convergence. Monte-Carlo is the method of choice for accurate yield estimation. Standard Monte-Carlo methods suffer from huge computational burden even though they are very accurate. Monte-Carlo efficiency is not high enough to use impartial for Analog Yield Optimization. Population initialization is a decisive task in...
QIEA-R (Quantum Inspired Evolutionary Algorithm with Real Codification) was proposed for solving numerical problems obtaining better results when compared with traditional EAs, DE and PSO algorithms. It is inspired on the concept of quantum superposition in order to reduce the number of evaluations. QIEA-R has two important steps: initialization of the quantum population and updating of the quantum...
An improved AdaBoost algorithm based on optimizing search in sample space is presented. Working with data in large scale need more time to compare samples for finding a threshold in the AdaBoost algorithm when using decision stump as a weak classifier. We used PSO algorithm to evolve and select best feature in sample space for a weak classifier to reduce time. The experiment results show that with...
In this paper we are continuing in our research to show mutual intersection of two different areas of research: complex network and evolutionary computation. This research parer is focused on possibility to convert run of evolution algorithm to a complex network inspired by ants. Such network can then be analyzed to get useful information about algorithm dynamics. In this paper we focused on one global...
With the proliferating development of heuristic methods, it has become challenging to choose the most suitable ones for an application at hand. This paper evaluates the performance of these algorithms available in Matlab, as it is problem dependent and parameter sensitive. Further, the paper attempts to address the challenge that there exists no satisfied benchmarks to evaluation all the algorithms...
In 1999, the so-called Strength Pareto Evolutionary Algorithm (SPEA) was introduced to find a set of approximate Pareto efficient solutions. SPEA uses a correction method for handling the constraints of combinatorial problems, resulting in an interruption of the evolutionary process. In this paper, we present an extension of the original algorithm, which keeps infeasible solutions in the population,...
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