Structured Memetic Automation for Online Human-like Social Behavior Learning

Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin's theory of natural selection and Dawkins' notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents' mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in this paper. Particularly, we focus on meme selection design where the commonly used elitist strategy is further improved by assimilating the notion of like-attracts-like in the human learning. We conduct experimental study on multiple problem domains and show the performance of the proposed MeMAS on human-like social behavior

The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems

The Travel Salesman Problem (TSP) consists in finding the minimal-length closed tour that connects the entire group of nodes of a given graph. We propose to solve such a combinatorial optimization problem with the AddACO algorithm: it is a version of the Ant Colony Optimization method that is characterized by a modified probabilistic law at the basis of the exploratory movement of the artificial insects. In particular, the ant decisional rule is here set to amount in a linear convex combination of competing behavioral stimuli and has therefore an additive form (hence the name of our algorithm), rather than the canonical multiplicative one. The AddACO intends to address two conceptual shortcomings that characterize classical ACO methods: (i) the population of artificial insects is in principle allowed to simultaneously minimize/maximize all migratory guidance cues (which is in implausible from a biological/ecological point of view) and (ii) a given edge of the graph has a null probability to be explored if at least one of the movement trait is therein equal to zero, i.e., regardless the intensity of the others (this in principle reduces the exploratory potential of the ant colony). Three possible variants of our method are then specified: the AddACO-V1, which includes pheromone trail and visibility as insect decisional variables, and the AddACO-V2 and the AddACO-V3, which in turn add random effects and inertia, respectively, to the two classical migratory stimuli. The three versions of our algorithm are tested on benchmark middle-scale TPS instances, in order to assess their performance and to find their optimal parameter setting. The best performing variant is finally applied to large-scale TSPs, compared to the naive Ant-Cycle Ant System, proposed by Dorigo and colleagues, and evaluated in terms of quality of the solutions, computational time, and convergence speed. The aim is in fact to show that the proposed transition probability, as long as its conceptual advantages, is competitive from a performance perspective, i.e., if it does not reduce the exploratory capacity of the ant population w.r.t. the canonical one (at least in the case of selected TSPs). A theoretical study of the asymptotic behavior of the AddACO is given in the appendix of the work, whose conclusive section contains some hints for further improvements of our algorithm, also in the perspective of its application to other optimization problems

A Method to Plan Group Tours with Joining and Forking

SEAL2006 : Asia-Pacific Conference on Simulated Evolution and Learning , Oct 15-18, 2006 , Hefei, ChinaGroup sightseeing has some advantages in terms of required budget and so on. Some travel agents provide package tours of group sightseeing, but participants have to follow a predetermined schedule in tour, and thus there may be no plan which perfectly satisfies the tourist's expectation. In this paper, we formalize a problem to find group sightseeing schedules for each user from given users’ preferences and time restrictions corresponding to each destination. We also propose a Genetic Algorithm-based algorithm to solve the problem. We implemented and evaluated the method, and confirmed that our algorithm finds efficient routes for group sightseeing

Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciónSéneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.Ingeniería, Industria y Construcció

Multi Objective Optimization of classification rules using Cultural Algorithms

AbstractClassification rule mining is the most sought out by users since they represent highly comprehensible form of knowledge. The rules are evaluated based on objective and subjective metrics. The user must be able to specify the properties of the rules. The rules discovered must have some of these properties to render them useful. These properties may be conflicting. Hence discovery of rules with specific properties is a multi objective optimization problem. Cultural Algorithm (CA) which derives from social structures, and which incorporates evolutionary systems and agents, and uses five knowledge sources (KS's) for the evolution process better suits the need for solving multi objective optimization problem. In the current study a cultural algorithm for classification rule mining is proposed for multi objective optimization of rules

Ant lion optimizer for solving optimal reactive power dispatch problem in power systems

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Algorithm portfolio based scheme for dynamic optimization problems

Since their first appearance in 1997 in the prestigious journal Science, algorithm portfolios have become a popular approach to solve static problems. Nevertheless and despite that success, they have not received much attention in Dynamic Optimization Problems (DOPs). In this work, we aim at showing these methods as a powerful tool to solve combinatorial DOPs. To this end, we propose a new algorithm portfolio for this type of problems that incorporates a learning scheme to select, among the metaheuristics that compose it, the most appropriate solver or solvers for each problem, configuration and search stage. This method was tested over 5 binary-coded problems (dynamic variants of OneMax, Plateau, RoyalRoad, Deceptive and Knapsack) and compared versus two reference algorithms for these problems (Adaptive Hill Climbing Memetic Algorithm and Self Organized Random Immigrants Genetic Algorithm). The results showed the importance of a good design of the learning scheme, the superiority of the algorithm portfolio against the isolated version of the metaheuristics that integrate it, and the competitiveness of its performance versus the reference algorithms.Spanish Government TIN2011-27696-C02-01 TEC2013-45585-C2-2-RAndalusian Government P11-TIC-8001European CommissionBasque Government PC2013-71AIbero-American University Association for Post Graduate Studies (AUIP

Corporate influence and the academic computer science discipline. [4: CMU]

Prosopographical work on the four major centers for computer research in the United States has now been conducted, resulting in big questions about the independence of, so called, computer science

Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction

This article embarks a study on multiagent transfer learning (TL) for addressing the specific challenges that arise in complex multiagent systems where agents have different or even competing objectives. Specifically, beyond the essential backbone of a state-of-the-art evolutionary TL framework (eTL), this article presents the novel TL framework with prediction (eTL-P) as an upgrade over existing eTL to endow agents with abilities to interact with their opponents effectively by building candidate models and accordingly predicting their behavioral strategies. To reduce the complexity of candidate models, eTL-P constructs a monotone submodular function, which facilitates to select Top-K models from all available candidate models based on their representativeness in terms of behavioral coverage as well as reward diversity. eTL-P also integrates social selection mechanisms for agents to identify their better-performing partners, thus improving their learning performance and reducing the complexity of behavior prediction by reusing useful knowledge with respect to their partners' mind universes. Experiments based on a partner-opponent minefield navigation task (PO-MNT) have shown that eTL-P exhibits the superiority in achieving higher learning capability and efficiency of multiple agents when compared to the state-of-the-art multiagent TL approaches