Antonio Berlanga

An approach to stopping criteria for multi-objective optimization evolutionary algorithms: The MGBM criterion

In this work we put forward a comprehensive study on the design of global stopping criteria for multi-objective optimization. We describe a novel stopping criterion, denominated MGBM criterion that combines the mutual domination rate (MDR) improvement indicator with a simplified Kalman filter that is used for evidence gathering process. The MDR indicator, which is introduced along, is a special purpose solution meant for the stopping task. It is capable of gauging the progress of the optimization with a low computational cost and therefore suitable for solving complex or many-objective problems. The viability of the proposal is established by comparing it with some other possible alternatives. It should be noted that, although the criteria discussed here are meant for MOPs and MOEAs, they could be easily adapted to other softcomputing or numerical methods by substituting the local improvement metric with a suitable one.

A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms

In this work we present a novel and efficient algorithm independent stopping criterion, called the MGBM criterion, suitable for Multiobjective Optimization Evolutionary Algorithms (MOEAs). The criterion, after each iteration of the optimization algorithm, gathers evidence of the improvement of the solutions obtained so far. A global (execution wise) evidence accumulation process inspired by recursive Bayesian estimation decides when the optimization should be stopped. Evidenceis collected using a novel relative improvement measure constructed on top of the Pareto dominance relations. The evidence gathered after each iteration is accumulated and updated following a rule based on a simplified version of a discrete Kalman filter. Our criterion is particularly useful in complex and/or high-dimensional problems where the traditional procedure of stopping after a predefined amount of iterations cannot be used and the waste of computational resources can induceto a detriment of the quality of the results. Although the criterion discussed here is meant for MOEAs,it can be easily adapted to other soft computing or numerical methods by substituting the local improvement metric with a suitable one.

Methods for Operations Planning in Airport Decision Support Systems

Simulation and decision support tools can help airport ground controllers to improve surface operations and safety, leading to enhancements in the process of traffic flow management. In this paper, two planning approaches for automatically finding the best routes and sequences for demanded operations are proposed and analyzed. These approaches are integrated into a general decision support system architecture. The problem addressed is the global management of departure operations, moving aircraft along airport taxiways between gate positions and runways. Two global optimization approaches have been developed together with a suitable problem representation: a modified time-space flow algorithm and a genetic algorithm, both aimed at minimizing the total ground delay. The capability and performance of these planning techniques have been illustrated on simulated samples of ground operations at Madrid Barajas International Airport.

Effective Evolutionary Algorithms for Many-Specifications Attainment: Application to Air Traffic Control Tracking Filters

This paper addresses a real-world engineering design requiring the application of effective and global optimization techniques. The problem it deals with is the design of nonlinear tracking filters under up to several hundreds of performance specifications. The suitability of different evolutionary computation techniques for solving multiobjective problems is explored, contrasting the performance achieved with recent multiobjective evolutionary algorithm (MOEAs) proposals and different aggregation schemes. In particular, a new scheme is proposed to build a fitness function based on an operator that selects worst cases of multiple specifications in different situations. They have been evaluated in the design of an air traffic control (ATC) tracking filter that should accomplish a specific normative with 264 specifications. Results show their performance in terms of effectiveness and computational load, comparing their capability to scale the problem with respect to problem size.

Introducing MONEDA: scalable multiobjective optimization with a neural estimation of distribution algorithm

In this paper we explore the model-building issue of multiobjective optimization estimation of distribution algorithms. We argue that model-building has some characteristics that differentiate it from other machine learning tasks. A novel algorithm called multiobjective neural estimation of distribution algorithm (MONEDA) is proposed to meet those characteristics. This algorithm uses a custom version of the growing neural gas (GNG) network specially meant for the model-building task. As part of this work, MONEDA is assessed with regard to other classical and state-of-the-art evolutionary multiobjective optimizers when solving some community accepted test problems.

Hydroelectric power plant management relying on neural networks and expert system integration

Abstract The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.

A probabilistic, discriminative and distributed system for the recognition of human actions from multiple views

This paper presents a distributed system for the recognition of human actions using views of the scene grabbed by different cameras. 2D frame descriptors are extracted for each available view to capture the variability in human motion. These descriptors are projected into a lower dimensional space and fed into a probabilistic classifier to output a posterior distribution of the action performed according to the descriptor computed at each camera. Classifier fusion algorithms are then used to merge the posterior distributions into a single distribution. The generated single posterior distribution is fed into a sequence classifier to make the final decision on the performed activity. The system can instantiate different algorithms for the different tasks, as the interfaces between modules are clearly defined. Results on the classification of the actions in the IXMAS dataset are reported. The accuracy of the proposed system is similar to state-of-the-art 3D methods, even though it uses only well-known 2D pattern recognition techniques and does not need to project the data into a 3D space or require camera calibration parameters.

MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms

We examine the model-building issue related to multi-objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current MOEDAs unviable when addressing optimization problems with many objectives. We propose a novel model-building growing neural gas (MB-GNG) network that is specially devised for properly dealing with that issue and therefore yields a better performance. Experiments are conducted in order to show from an empirical point of view the advantages of the new algorithm.

InContexto: Multisensor Architecture to Obtain People Context from Smartphones

The way users intectact with smartphones is changing after the improvements made in their embedded sensors. Increasingly, these devices are being employed as tools to observe individuals habits. Smartphones provide a great set of embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. This paper aims to describe a distributed architecture, called inContexto, to recognize user context information using mobile phones. Moreover, it aims to infer physical actions performed by users such as walking, running, and still. Sensory data is collected by HTC magic application made in Android OS, and it was tested achieving about 97% of accuracy classifying five different actions (still, walking and running).

Solving complex high-dimensional problems with the multi-objective neural estimation of distribution algorithm

The multi-objective optimization neural estimation of distribution algorithm (MONEDA) was devised with the purpose of dealing with the model-building issues of MOEDAs and, therefore address their scalability. In this paper we put forward a comprehensive set of experiments that intends to compare MONEDA with similar approaches when solving complex community accepted MOPs. In particular, we deal with the Walking Fish Group scalable test problem set (WFG). These tests aim to establish the optimizing capacity of MONEDA and the consistency as an optimization method. The fundamental conclusion of these assessment is that we provide strong evidences of the viability of MONEDA for handling hard and complex high-dimensional problems and its superior performance when compared to similar approaches. In spite of the fact that obviously further studies are necessary, these extensive experiments have provided solid ground for the use of MONEDA in more ambitious real-world applications.