We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

An Introduction to MultiAgent Systems.

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the literature, the paper presents a categorization of the techniques used to parallelize GAs, and shows examples of all of them. However, since the majority of the research in this field has concentrated on parallel GAs with multiple populations, the survey focuses on this type of algorithms. Also, the paper describes some of the most significant problems in modeling and designing multi-population parallel GAs and presents some recent advancements.

/pdf/a-survey-of-parallel-genetic-algorithms-rh37ea0cwr.pdf

A Survey of Parallel Genetic Algorithms

In this paper we consider Maximal Lifetime Coverage Problem in Wireless Sensor Networks which is formulated as a scheduling problem related to activity of sensors equipped at battery units and monitoring a two-dimensional space in time. The problem is known as an NP-hard and to solve it we propose two heuristics which use specific knowledge about the problem. The first one is proposed by us stochastic greedy algorithm and the second one is metaheuristic known as Simulated Annealing. The performance of both algorithms is verified by a number of numerical experiments. Comparison of the results show that while both algorithms provide results of similar quality, but greedy algorithm is slightly better in the sense of computational time complexity.

/pdf/scheduling-sensors-activity-in-wireless-sensor-networks-2w22wvwrax.pdf

Scheduling Sensors Activity in Wireless Sensor Networks

In this paper we present architecture of recently built experimental anomaly detection system based on the paradigm of artificial immune system and working in a network environment. We show how network traffic data are mapped into antibodies or antigens of artificial immune system and how similarities between signatures of attackers and antibodies are measured. We present an example of the work of the system in the real network environment.

Anomaly Detection System for Network Security: Immunity-based Approach

We consider a distributed system modeled by the second-order Cellular Automata (CA) and interpreted as a multi-agent system, where interactions between agents are defined by a spatial Prisoner’s Dilemma game. The idea of the second-order CA is based on the concept “adapt to the best neighbor. Each agent uses some strategy for the selection of actions used against opponents and can change it during the iterated game. An agent acts in such a way to maximize its income. We intend to study conditions of emerging collective behavior in such systems measured by the average total payoff of agents in the game or by an equivalent measure–the total number of cooperating players. These measures are the external criterion of the game, and players acting selfishly are not aware of them. We show experimentally that collective behavior in such systems can emerge if some conditions related to the game are fulfilled. We propose to introduce an income sharing mechanism to the game, giving a possibility to share incomes locally by agents. We present the results of an experimental study showing that the sharing mechanism is a distributed optimization algorithm that significantly improves the capabilities of emerging collective behavior on a wide range of the game parameters.

Behavior Optimization in Large Distributed Systems Modeled by Cellular Automata

An approach based on an application of Nature inspired algorithms to the problem of reconstructing human face images from ones with only partial information is presented in the paper. The two-dimensional cellular automata CA is used to represent images, while genetic algorithm GA is used to discover CA rules which will be able to reconstruct an original image from e.g. destroyed or modified images. Conducted experiments show that found in the learning process CA rules allow to reconstruct images with 70% damaged pixels. Moreover, the results indicate that the found rules are useful to reconstruct the other images not presented during evolutionary learning process.

Reconstructing Images with Nature Inspired Algorithms

The paper presents the GAVis (Genetic Algorithm Visualization) system designed to support solving combinatorial optimization problems using evolutionary algorithms. One of the main features of the system is tracking complex dependencies between parameters of an implemented algorithm with use of visualization. The role of the system is shown by its application to solve two problems: multiprocessor scheduling problem and Travelling Salesman Problem (TSP).

Franciszek Seredynski

Papers

Scheduling Sensors Activity in Wireless Sensor Networks

Anomaly Detection System for Network Security: Immunity-based Approach

Behavior Optimization in Large Distributed Systems Modeled by Cellular Automata

Reconstructing Images with Nature Inspired Algorithms

GAVis System Supporting Visualization, Analysis and Solving Combinatorial Optimization Problems Using Evolutionary Algorithms