Pattern discovery in protein interaction networks can reveal crucial biological knowledge on the inner workings of cellular machinery. Although far from complete, extracting meaningful patterns from proteomic networks is a non-trivial task due to their size-complexity. This paper proposes a computational framework to efficiently discover topologically-similar patterns from large proteomic networks using Particle Swarm Optimization (PSO). PSO is a robust and low-cost optimization technique that demonstrated to work effectively on the complex, mostly sparse proteomic networks. The resulting topologically-similar patterns of close proximity are utilized to systematically predict new high-confidence protein-protein interactions (PPIs). The proposed PSO-based PPI prediction method (3PI) managed to predict high-confidence PPIs, validated by more than one computational/experimental source, through a proposed PPI knowledge transfer process between topologically-similar interaction patterns of close proximity. In three case studies, over 50% of the predicted interactions for EFGR, ERBB2, ERBB3, GRB2 and UBC are overlapped with publically available interaction databases, ~80% of the predictions are found among the Top 1% results of another PPI prediction method and their genes are significantly co-expressed across different tissues. Moreover, the only single prediction example that did not overlap with any of our validation sources was recently experimentally supported by two PubMed publications.

Keepaway soccer is a challenging robot control task that has been widely used as a benchmark for evaluating multi-agent learning systems. The majority of research in this domain has been from the perspective of reinforcement learning (function approximation) and neuroevolution. One of the challenges under multi-agent tasks such as keepaway is to formulate effective mechanisms for diversity maintenance. Indeed the best results to date on this task utilize some form of neuroevolution with genotypic diversity. In this work, a symbiotic framework for evolving teams of programs is utilized with both genotypic and behavioural forms of diversity maintenance considered. Specific contributions of this work include a simple scheme for characterizing genotypic diversity under teams of programs and its comparison to behavioural formulations for diversity under the keepaway soccer task. Unlike previous research concerning diversity maintenance in genetic programming (GP), we are explicitly interested in solutions taking the form of teams of programs.

Today, people benefit from utilizing data mining technologies, such as association rule mining methods, to find valuable knowledge residing in a large amount of data. However, they also face the risk of exposing sensitive or confidential information, when data is shared among different organizations. Thus, a question arise: how can we prevent that sensitive knowledge is discovered, while ensuring that ordinary non-sensitive knowledge can be mined to the maximum extent possible. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A new hiding method based evolutionary multi-objective optimization (EMO) is proposed and the side effects generated by the hiding process are formulated as optimization goals. EMO is used to find candidate transactions to modify so that side effects are minimized. Comparative experiments with exact methods on real datasets demonstrated that the proposed method can hide sensitive rules with fewer side effects.

In this paper we introduce new types of square-piece jigsaw puzzles, where in addition to the unknown location and orientation of each piece, a piece might also need to be flipped. These puzzles, which are associated with a number of real world problems, are considerably harder, from a computational standpoint. Specifically, we present a novel generalized genetic algorithm (GA)-based solver that can handle puzzle pieces of unknown location and orientation (Type 2 puzzles) and (two-sided) puzzle pieces of unknown location, orientation, and face (Type 4 puzzles). To the best of our knowledge, our solver provides a new state-of-the-art, solving previously attempted puzzles faster and far more accurately, handling puzzle sizes that have never been attempted before, and assembling the newly introduced two-sided puzzles automatically and effectively. This paper also presents, among other results, the most extensive set of experimental results, compiled as of yet, on Type 2 puzzles.

Software design is crucial to successful software development, yet is a demanding multi-objective problem for software engineers. In an attempt to assist the software designer, interactive (i.e. human in-the-loop) meta-heuristic search techniques such as evolutionary computing have been applied and show promising results. Recent investigations have also shown that Ant Colony Optimization (ACO) can outperform evolutionary computing as a potential search engine for interactive software design. With a limited computational budget, ACO produces superior candidate design solutions in a smaller number of iterations. Building on these findings, we propose a novel interactive ACO (iACO) approach to assist the designer in early lifecycle software design, in which the search is steered jointly by subjective designer evaluation as well as machine fitness functions relating the structural integrity and surrogate elegance of software designs. Results show that iACO is speedy, responsive and highly effective in enabling interactive, dynamic multi-objective search in early lifecycle software design. Study participants rate the iACO search experience as compelling. Results of machine learning of fitness measure weightings indicate that software design elegance does indeed play a significant role in designer evaluation of candidate software design. We conclude that the evenness of the number of attributes and methods among classes (NAC) is a significant surrogate elegance measure, which in turn suggests that this evenness of distribution, when combined with structural integrity, is an implicit but crucial component of effective early lifecycle software design.

This paper studies the applicability of evolutionary algorithms, particularly, the evolution strategies family in order to estimate a degradation parameter in the shear design of reinforced concrete members. This problem represents a great computational task and is highly relevant in the framework of the structural engineering that for the first time is solved using genetic algorithms. You are viewing a draft, the authors appreciate corrections, comments and suggestions to this work.

In the context of Noisy Multi-Objective Optimization, dealing with uncertainties requires the decision maker to define some preferences about how to handle them, through some statistics (e.g., mean, median) to be used to evaluate the qualities of the solutions, and define the corresponding Pareto set. Approximating these statistics requires repeated samplings of the population, drastically increasing the overall computational cost. To tackle this issue, this paper proposes to directly estimate the probability of each individual to be selected, using some Hoeffding races to dynamically assign the estimation budget during the selection step. The proposed racing approach is validated against static budget approaches with NSGA-II on noisy versions of the ZDT benchmark functions.

Finding the shortest plan for a given planning problem is extremely hard. We present a domain independent approach for plan optimisation based on Genetic Programming. The algorithm is seeded with correct plans created by hand-encoded heuristic policy sets. The plans are very unlikely to be optimal but are created quickly. The suboptimal plans are then evolved using a generational algorithm towards the optimal plan. We present initial results from Blocks World and found that GP method almost always improved sub-optimal plans, often drastically.

Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for generating new and entertaining board games, provided an initial search space is given to the evolutionary algorithm.

In this paper, we consider the problem of finding a minimum common partition of two strings. The problem has its application in genome comparison. As it is an NPhard, discrete combinatorial optimization problem, we employ a metaheuristic technique, namely, MAX-MIN ant system to solve this problem. To achieve better efficiency we first map the problem instance into a special kind of graph. Subsequently, we employ a MAX-MIN ant system to achieve high quality solutions for the problem. Experimental results show the superiority of our algorithm in comparison with the state of art algorithm in the literature. The improvement achieved is also justified by standard statistical test. Keywords: Ant Colony Optimization, Stringology, Genome sequencing, Combinatorial Optimization, Swarm Intelligence, String partitioning 1. Introduction String comparison is one of the important problems in Computer Science with diverse applications in different areas including Genome Sequencing, text processing and compressions. In this paper, we address the problem of finding a minimum common partition (MCSP) of two strings. MCSP is closely related to genome arrangement which is an important topic in computational biology.