Abstrak The importance of selecting relevant features for data modeling has been recognized already in machine learning. This paper discusses the application of an evolutionary-based feature selection method in order to generate input data for unsupervised learning in DARA (Dynamic Aggregation of Relational Attributes). The feature selection process which is based on the evolutionary algorithm ...

1371 Stable On-Line Evolutionary Learning of NN-MLP Qiangfu Zhao Abstract| To design the nearest neighbor based multilayer perceptron (NN-MLP) e ciently, the author has proposed a non-genetic based evolutionary algorithm called the R4|rule. For o -line learning, the R4|rule can produce the smallest or nearly smallest networks with high generalization ability by iteratively performing four basic...

The supervised learning methods applying evolutionary algorithms to generate knowledge model are extremely costly in time and space. Fundamentally, this high computational cost is fundamentally due to the evaluation process that needs to go through the whole datasets to assess their goodness of the genetic individuals. Often, this process carries out some redundant operations which can be avoid...

Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range shift in organism, range of invasive species influenced by climate change are important parameters in understanding the impact of climate change. However, success of machine learning-based approache...

The design of intelligent agents by means of reinforcement learning is studied in this paper. A relational reinforcement learning algorithm is used to achieve a compact knowledge representation. Moreover, this approach allows to improve the learning performance by augmenting the algorithm with the so-called background knowledge. A case study on simulated physical robotic agents is performed and...

Both Evolutionary Dynamic Optimization (EDO) methods and Reinforcement Learning (RL) methods tackle forms of Sequential Decision Making Problems (SDMPs), yet with different key assumptions. In this paper, we combine the strength of both EDO methods and RL methods to develop a new algorithm for SDMPs. Assuming that the environmental state is observable and that a computational model of the rewar...

The claim of the paper is that Evolutionary Learning is a source of diverse hypotheses “for free”, and this specificity can be used to combine in an ensemble the hypotheses learned in independent runs. The aim of our algorithm named Broger (Bagging-ROC GEnetic LEarneR) consists of optimizing the Area Under the ROC Curve using Evolutionary Learning. This paper first presents the theoretical fram...

Genetic Programming (GP) is an evolutionary algorithm commonly used for machine learning tasks. In this paper we present a method that allows GP to transform the representation of a large-scale machine learning dataset into a more compact representation, by means of processing features from the original representation at individual level. We develop as a proof of concept of this method an autoe...

Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning tasks, TD methods require a function approximator to represent the value function. However, using function approximators requires manually making crucial representational decisions. This paper investigates evolutionary...

Structural optimization, when approached by conventional (gradient based) minimization algorithms presents several difficulties, mainly related to computational aspects for the huge number of nonlinear analyses required, that regard both Objective Functions (OFs) and Constraints. Moreover, from the early '80s to today's, Evolutionary Algorithms have been successfully developed and applied as a ...