Alberto Paolo Tonda

An evolutionary framework for routing protocol analysis in wireless sensor networks

Wireless Sensor Networks (WSNs) are widely adopted for applications ranging from surveillance to environmental monitoring. While powerful and relatively inexpensive, they are subject to behavioural faults which make them unreliable. Due to the complex interactions between network nodes, it is difficult to uncover faults in a WSN by resorting to formal techniques for verification and analysis, or to testing. This paper proposes an evolutionary framework to detect anomalous behaviour related to energy consumption in WSN routing protocols. Given a collection protocol, the framework creates candidate topologies and evaluates them through simulation on the basis of metrics measuring the radio activity on nodes. Experimental results using the standard Collection Tree Protocol show that the proposed approach is able to unveil topologies plagued by excessive energy depletion over one or more nodes, and thus could be used as an offline debugging tool to understand and correct the issues before network deployment and during the development of new protocols.

Towards automated malware creation: code generation and code integration

This short paper proposes two different ways for exploiting an evolutionary algorithm to devise malware: the former targeting heuristic-based anti-virus scanner; the latter optimizing a Trojan attack. An extended internal on the same the subject can be downloaded from http://www.cad.polito.it/downloads/

Towards automatic StarCraft strategy generation using genetic programming

Among Real-Time Strategy games few titles have enjoyed the continued success of StarCraft. Many research lines aimed at developing Artificial Intelligences, or “bots”, capable of challenging human players, use StarCraft as a platform. Several characteristics make this game particularly appealing for researchers, such as: asymmetric balanced factions, considerable complexity of the technology trees, large number of units with unique features, and potential for optimization both at the strategical and tactical level. In literature, various works exploit evolutionary computation to optimize particular aspects of the game, from squad formation to map exploration; but so far, no evolutionary approach has been applied to the development of a complete strategy from scratch. In this paper, we present the preliminary results of StarCraftGP, a framework able to evolve a complete strategy for StarCraft, from the building plan, to the composition of squads, up to the set of rules that define the bots behavior during the game. The proposed approach generates strategies as C++ classes, that are then compiled and executed inside the OpprimoBot open-source framework. In a first set of runs, we demonstrate that StarCraftGP ultimately generates a competitive strategy for a Zerg bot, able to defeat several human-designed bots.

Bayesian network structure learning from limited datasets through graph evolution

Bayesian networks are stochastic models, widely adopted to encode knowledge in several fields. One of the most interesting features of a Bayesian network is the possibility of learning its structure from a set of data, and subsequently use the resulting model to perform new predictions. Structure learning for such models is a NP-hard problem, for which the scientific community developed two main approaches: score-and-search metaheuristics, often evolutionary-based, and dependency-analysis deterministic algorithms, based on stochastic tests. State-of-the-art solutions have been presented in both domains, but all methodologies start from the assumption of having access to large sets of learning data available, often numbering thousands of samples. This is not the case for many real-world applications, especially in the food processing and research industry. This paper proposes an evolutionary approach to the Bayesian structure learning problem, specifically tailored for learning sets of limited size. Falling in the category of score-and-search techniques, the methodology exploits an evolutionary algorithm able to work directly on graph structures, previously used for assembly language generation, and a scoring function based on the Akaike Information Criterion, a well-studied metric of stochastic model performance. Experimental results show that the approach is able to outperform a state-of-the-art dependency-analysis algorithm, providing better models for small datasets.

A benchmark for cooperative coevolution

Cooperative co-evolution algorithms (CCEA) are a thriving sub-field of evolutionary computation. This class of algorithms makes it possible to exploit more efficiently the artificial Darwinist scheme, as soon as an optimisation problem can be turned into a co-evolution of interdependent sub-parts of the searched solution. Testing the efficiency of new CCEA concepts, however, it is not straightforward: while there is a rich literature of benchmarks for more traditional evolutionary techniques, the same does not hold true for this relatively new paradigm. We present a benchmark problem designed to study the behavior and performance of CCEAs, modeling a search for the optimal placement of a set of lamps inside a room. The relative complexity of the problem can be adjusted by operating on a single parameter. The fitness function is a trade-off between conflicting objectives, so the performance of an algorithm can be examined by making use of different metrics. We show how three different cooperative strategies, Parisian Evolution, Group Evolution and Allopatric Group Evolution, can be applied to the problem. Using a Classical Evolution approach as comparison, we analyse the behavior of each algorithm in detail, with respect to the size of the problem.

On the functional test of Branch Prediction Units based on Branch History Table

Branch Prediction Units (BPUs) are highly efficient modules that can significantly decrease the negative impact of branches in superscalar and RISC processors. Traditional test solutions, mainly based on scan test, are often inadequate to tackle the complexity of these architectures, especially when dealing with delay faults that require at-speed stimuli application. Moreover, scan test does not represent a viable solution when Incoming Inspection or on-line test are considered. In this paper a functional approach targeting BPU test is proposed, allowing to generate a suitable test program whose effectiveness is independent on the specific implementation of the BPU. The effectiveness of the approach is validated on a Branch History Table (BHT) resorting to an open-source computer architecture simulator and to an ad hoc developed HDL testbench. Experimental results show that the proposed method is able to thoroughly test the BHT, reaching complete static fault coverage.

The tradeoffs between data delivery ratio and energy costs in wireless sensor networks: a multi-objectiveevolutionary framework for protocol analysis

Wireless sensor network (WSN) routing protocols, e.g., the Collection Tree Protocol (CTP), are designed to adapt in an ad-hoc fashion to the quality of the environment. WSNs thus have high internal dynamics and complex global behavior. Classical techniques for performance evaluation (such as testing or verification) fail to uncover the cases of extreme behavior which are most interesting to designers. We contribute a practical framework for performance evaluation of WSN protocols. The framework is based on multi-objective optimization, coupled with protocol simulation and evaluation of performance factors. For evaluation, we consider the two crucial functional and non-functional performance factors of a WSN, respectively: the ratio of data delivery from the network (DDR), and the total energy expenditure of the network (COST). We are able to discover network topological configurations over which CTP has unexpectedly low DDR and/or high COST performance, and expose full Pareto fronts which show what the possible performance tradeoffs for CTP are in terms of these two performance factors. Eventually, Pareto fronts allow us to bound the state space of the WSN, a fact which provides essential knowledge to WSN protocol designers.

On the Generation of Functional Test Programs for the Cache Replacement Logic

Caches are crucial components in modern processors (both stand-alone or integrated into SoCs) and their test is a challenging task, especially when addressing complex and high-frequency devices. While the test of the memory array within the cache is usually accomplished resorting to BIST circuitry implementing March test inspired solutions, testing the cache controller logic poses some specific issues, mainly stemming from its limited accessibility. One possible solution consists in letting the processor execute suitable test programs, allowing the detection of possible faults by looking at the results they produce. In this paper we face the issue of generating suitable programs for testing the replacement logic in set-associative caches that implement a deterministic replacement policy. A test program generation approach based on modeling the replacement mechanism as a Finite State Machine (FSM) is proposed. Experimental results with a cache implementing a LRU policy are provided to assess the effectiveness of the method.

A novel methodology for diversity preservation in evolutionary algorithms

In this paper we describe an improvement of an entropy-based diversity preservation approach for evolutionary algorithms. This approach exploits the information contained not only in the parts that compose an individual, but also in their position and relative order. We executed a set of preliminary experiments in order to test the new approach, using two different problems in which diversity preservation plays a major role in obtaining good solutions.

Lamps : a test problem for cooperative coevolution

We present an analysis of the behaviour of Cooperative Co-evolution algorithms (CCEAs) on a simple test problem, that is the optimal placement of a set of lamps in a square room, for various problems sizes. Cooperative Co-evolution makes it possible to exploit more efficiently the artificial Darwinism scheme, as soon as it is possible to turn the optimisation problem into a co-evolution of interdependent sub-parts of the searched solution. We show here how two cooperative strategies, Group Evolution (GE) and Parisian Evolution (PE) can be built for the lamps problem. An experimental analysis then compares a classical evolution to GE and PE, and analyses their behaviour with respect to scale.