Crowd motion in surveillance videos is comparable to heat motion of basic particles. Inspired by that, we introduce Boltzmann Entropy to measure crowd motion in optical flow field so as to detect abnormal collective behaviors. As a result, the collective crowd moving pattern can be represented as a time series. We found that when most people behave anomaly, the entropy value will increase drastically. Thus, a threshold can be applied to the time series to identify abnormal crowd commotion in a simple and efficient manner without machine learning. The experimental results show promising performance compared with the state of the art methods. The system works in real time with high precision.

In this paper we propose a wrapper based PSO method for gene selection in microarray datasets, where we gradually refine the feature (gene) space from a very coarse level to a fine grained one, by reducing the gene set at each step of the algorithm. We use the linear support vector machine weight vector to serve as the initial gene pool selection. In addition, we also examine integration of other filter based ranking methods with our proposed approach. Experiments on publicly available datasets, Colon, Leukemia and T2D show that our approach selects only a very small subset of genes while yielding substantial improvements in accuracy over state-of-the-art evolutionary methods.

Grammatical Evolution (GE) is that area of genetic algorithms that evolves computer programs in high-level languages possessing a BNF grammar. In this work, we present GEF (“Grammatical Evolution for the Finch”), a system that employs grammatical evolution to create a Finch robot controller program in Java. The system uses both the traditional GE model as well as employing extensions and augmentations that push the boundaries of goal-oriented contexts in which robots typically act including a meta-level handler that fosters a level of self-awareness in the robot. To handle contingencies, the GEF system has been endowed with the ability to perform meta-level jumps. When confronted with unplanned events and dynamic changes in the environment, our robot will automatically transition to pursue another goal, changing fitness functions, and generate and invoke operating system level scripting to facilitate the change. The robot houses a raspberry pi controller that is capable of executing one (evolved) program while wirelessly receiving another over an asynchronous client. This work is part of an overall project that involves planning for contingencies. In this poster, we present the development framework and system architecture of GEF, including the newly discovered meta-level handler, as well as some other system successes, failures, and insights.

Ensemble learning is among the state-of-the-art learning techniques, which trains and combines many base learners. Ensemble pruning removes some of the base learners of an ensemble, and has been shown to be able to further improve the generalization performance. However, the two goals of ensemble pruning, i.e., maximizing the generalization performance and minimizing the number of base learners, can conflict when being pushed to the limit. Most previous ensemble pruning approaches solve objectives that mix the two goals. In this paper, motivated by the recent theoretical advance of evolutionary optimization, we investigate solving the two goals explicitly in a bi-objective formulation and propose the PEP (Pareto Ensemble Pruning) approach. We disclose that PEP does not only achieve significantly better performance than the state-of-the-art approaches, and also gains theoretical support.

In this paper we focus on finding high quality solutions for the problem of maximum partitioning of graphs with supply and demand (MPGSD). There is a growing interest for the MPGSD due to its close connection to problems appearing in the field of electrical distribution systems, especially for the optimization of self-adequacy of interconnected microgrids. We propose an ant colony optimization algorithm for the problem. With the goal of further improving the algorithm we combine it with a previously developed correction procedure. In our computational experiments we evaluate the performance of the proposed algorithm on both trees and general graphs. The tests show that the method manages to find optimal solutions in more than 50% of the problem instances, and has an average relative error of less than 0.5% when compared to known optimal solutions. Keywords: Ant Colony Optimization, Microgrid, Graph Partitioning, Demand Vertex, Supply Vertex, Combinatorial Optimization 1. Introduction In recent years the research in the field of smart grids has had a significant increase in exploring the concept of interconnected microgrids [1].

Saving energy is a major concern. Hence, it is fundamental to design and construct buildings that are energy-efficient. It is known that the early stage of architectural design has a significant impact on this matter. However, it is complex to create designs that are optimally energy efficient, and at the same time balance other essential criterias such as economics, space, and safety. One state-of-the art approach is to create parametric designs, and use a genetic algorithm to optimize across different objectives. We further improve this method, by aggregating the solutions of multiple agents. We evaluate diverse teams, composed by different agents; and uniform teams, composed by multiple copies of a single agent. We test our approach across three design cases of increasing complexity, and show that the diverse team provides a significantly larger percentage of optimal solutions than single agents.

Saving energy is a major concern. Hence, it is fundamental to design and construct buildings that are energy-efficient. It is known that the early stage of architectural design has a significant impact on this matter. However, it is complex to create designs that are optimally energy efficient, and at the same time balance other essential criterias such as economics, space, and safety. One state-of-the art approach is to create parametric designs, and use a genetic algorithm to optimize across different objectives. We further improve this method, by aggregating the solutions of multiple agents. We evaluate diverse teams, composed by different agents; and uniform teams, composed by multiple copies of a single agent. We test our approach across three design cases of increasing complexity, and show that the diverse team provides a significantly larger percentage of optimal solutions than single agents.

Much AI research on computational sustainability has focused on monitoring, modeling, analysis, and optimization of existing systems and processes. In this article, we present another exciting and promising paradigm for AI research on computational sustainability that emphasizes design of new systems and processes, and, in particular, on biologically inspired design. We first characterize biologically inspired design, then examine its relationship with environmental sustainability, next present a computational model of the process of biologically inspired design, and finally describe a few computational systems for supporting biologically inspired design practice.

In this paper, we focus on developing efficient sensitivity analysis methods for a computationally expensive objective function $f(x)$ in the case that the minimization of it has just been performed. Here "computationally expensive" means that each of its evaluation takes significant amount of time, and therefore our main goal to use a small number of function evaluations of $f(x)$ to further infer the sensitivity information of these different parameters. Correspondingly, we consider the optimization procedure as an adaptive experimental design and re-use its available function evaluations as the initial design points to establish a surrogate model $s(x)$ (or called response surface). The sensitivity analysis is performed on $s(x)$, which is an lieu of $f(x)$. Furthermore, we propose a new local multivariate sensitivity measure, for example, around the optimal solution, for high dimensional problems. Then a corresponding "objective-oriented experimental design" is proposed in order to make the generated surrogate $s(x)$ better suitable for the accurate calculation of the proposed specific local sensitivity quantities. In addition, we demonstrate the better performance of the Gaussian radial basis function interpolator over Kriging in our cases, which are of relatively high dimensionality and few experimental design points. Numerical experiments demonstrate that the optimization procedure and the "objective-oriented experimental design" behavior much better than the classical Latin Hypercube Design. In addition, the performance of Kriging is not as good as Gaussian RBF, especially in the case of high dimensional problems.

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems.