Group evolution: Emerging synergy through a coordinated effort

Abstract-A huge number of optimization problems, in the CAD area as well as in many other fields, require a solution composed by a set of structurally homogeneous elements. Each element tackles a subset of the original task, and they cumulatively solve the whole problem. Sub-tasks, however, have exactly the same structure, and the splitting is completely arbitrary. Even the number of sub-tasks is not known and cannot be determined a-priori. Individual elements are structurally homogeneous, and their contribution to the main solution can be evaluated separately. We propose an evolutionary algorithm able to optimize groups of individuals for solving this class of problems. An individual of the best solution may be sub-optimal when considered alone, but the set of individuals cumulatively represent the optimal group able to completely solve the whole problem. Results of preliminary experiments show that our algorithm performs better than other techniques commonly applied in the CAD fiel

Post-silicon failing-test generation through evolutionary computation

The incessant progress in manufacturing technology is posing new challenges to microprocessor designers. Several activities that were originally supposed to be part of the pre-silicon design phase are migrating after tape-out, when the first silicon prototypes are available. The paper describes a post-silicon methodology for devising functional failing tests. Therefore, suited to be exploited by microprocessor producer to detect, analyze and debug speed paths during verification, speed-stepping, or other critical activities. The proposed methodology is based on an evolutionary algorithm and exploits a versatile toolkit named µGP. The paper describes how to take into account complex hardware characteristics and architectural details of such complex devices. The experimental evaluation clearly demonstrates the potential of this line of researc

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

Evolutionary failing-test generation for modern microprocessors

The incessant progress in manufacturing technology is posing new challenges to microprocessor designers. Nowadays, comprehensive verification of a chip can only be performed after tape-out, when the first silicon prototypes are available. Several activities that were originally supposed to be part of the pre-silicon design phase are migrating to this post-silicon time as well. The short paper describes a post-silicon methodology that can be exploited to devise functional failing tests. Such tests are essential to analyze and debug speed paths during verification, speed-stepping, and other critical activities. The proposed methodology is based on the Genetic Programming paradigm, and exploits a versatile toolkit named μGP. The paper demonstrates that an evolutionary algorithm can successfully tackle a significant and still open industrial problem. Moreover, it shows how to take into account complex hardware characteristics and architectural details of such complex device

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 scal

Portfolio Optimization, a Decision-Support Methodology for Small Budgets

Several machine learning paradigms have been applied to financial forecasting, attempting to predict the market's behavior, with the final objective of profiting from trading shares. While anticipating the performance of such a complex system is far from trivial, this issue becomes even harder when the investors do not have large amounts of money available. In this paper, we present an evolutionary portfolio optimizer for the management of small budgets. The expected returns are modeled resorting to Multi-layer Perceptrons, trained on past market data, and the portfolio composition is chosen by approximating the solution to a multi-objective constrained problem. An investment simulator is then used to measure the portfolio performance. The proposed approach is tested on real-world data from Milan stock exchange, exploiting information from January 2000 to June 2010 to train the framework, and data from July 2010 to August 2011 to validate it. The presented tool is finally proven able to obtain a more than satisfying profit for the considered time fram

Automatic Generation of Software-based Functional Failing Test for Speed Debug and On-silicon Timing Verification2011 12th International Workshop on Microprocessor Test and Verification

The 40 years since the appearance of the Intel 4004 deeply changed how microprocessors are designed. Today, essential steps in the validation process are performed relying on physical dices, analyzing the actual behavior under appropriate stimuli. This paper presents a methodology that can be used to devise assembly programs suitable for a range of on-silicon activities, like speed debug, timing verification or speed binning. The methodology is fully automatic. It exploits the feedback from the microprocessor under examination and does not rely on information about its microarchitecture, nor does it require design-for-debug features. The experimental evaluation performed on a Intel Pentium Core i7-950 demonstrates the feasibility of the approach

Evolving Individual Behavior in a Multi-Agent Traffic Simulator

Chapter 2In this paper, we illustrate the use of evolutionary agents in a multi-agent system designed to describe the behavior of car drivers. Each agent has the selfish objective to reach its destination in the shortest time possible, and a preference in terms of paths to take, based on the presence of other agents and on the width of the roads. Those parameters are changed with an evolutionary strategy, to mimic the adaptation of a human driver to different traffic conditions. The system proposed is then tested by giving the agents the ability to perceive the presence of other agents in a given radius. Experimental results show that knowing the position of all the car drivers in the map leads the agents to obtain a better performance, thanks to the evolution of their behavior. Even the system as a whole gains some benefits from the evolution of the agents’ individual choices

Universal information distance for genetic programming

This paper presents a genotype-level distance metric for Genetic Programming (GP) based on the symmetric difference concept: first, the information contained in individuals is expressed as a set of symbols (the content of each node, its position inside the tree, and recurring parent-child structures); then, the difference between two individuals is computed considering the number of elements belonging to one, but not both, of their symbol sets