Driving Cars by Means of Genetic Algorithms

Proceedings of: 10th International Conference on Parallel Problem Solving From Nature, PPSN 2008. Dortmund, Germany, September 13-17, 2008The techniques and the technologies supporting Automatic Vehicle Guidance are an important issue. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improvement of the safety of the car, reducing emission or fuel consumption or optimizing driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics, consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. As a first goal we want to automatically learn to drive, by means of genetic algorithms, optimizing lap times while driving through three different circuits.Publicad

Nature-inspired synthesis of rational protocols

Proceedings of: 10th International Conference on Parallel Problem Solving from Nature (PPSN 2008), Dortmund, Germany, September 13-17, 2008.Rational cryptography is an emerging field which combines aspects traditionally related to security with concepts described in economic theoretical frameworks. For example, it applies game theory concepts to address security problems arising when executing cryptographic protocols. The aim is to replace the assumption of a worst-case attacker by the notion of rational agents that try to maximize their payoffs. In this work, we define a formal framework and a meta--heuristic technique for the automated synthesis of multi-party rational exchange security (M-RES) protocols. We provide experimental results for a simple scenario where a 3-party rational exchange protocol is automatically designed.Publicad

An Experimental Study of Adaptive Control for Evolutionary Algorithms

The balance of exploration versus exploitation (EvE) is a key issue on evolutionary computation. In this paper we will investigate how an adaptive controller aimed to perform Operator Selection can be used to dynamically manage the EvE balance required by the search, showing that the search strategies determined by this control paradigm lead to an improvement of solution quality found by the evolutionary algorithm

A steady-state genetic algorithm with resampling for noisy inventory control

Noisy fitness functions occur in many practical applications of evolutionary computation. A standard technique for solving these problems is fitness resampling but this may be inefficient or need a large population, and combined with elitism it may overvalue chromosomes or reduce genetic diversity. We describe a simple new resampling technique called Greedy Average Sampling for steady-state genetic algorithms such as GENITOR. It requires an extra runtime parameter to be tuned, but does not need a large population or assumptions on noise distributions. In experiments on a well-known Inventory Control problem it performed a large number of samples on the best chromosomes yet only a small number on average, and was more effective than four other tested technique

Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient

A fitness assignment process transforms the features (such as the objective value) of a candidate solution to a scalar fitness, which then is the basis for selection. Under Frequency Fitness Assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in selection steps and is subject to minimization. FFA creates algorithms that are not biased towards better solutions and are invariant under all injective transformations of the objective function value. We investigate the impact of FFA on the performance of two theory-inspired, state-of-the-art EAs, the Greedy (2+1) GA and the Self-Adjusting (1+(lambda,lambda)) GA. FFA improves their performance significantly on some problems that are hard for them. In our experiments, one FFA-based algorithm exhibited mean runtimes that appear to be polynomial on the theory-based benchmark problems in our study, including traps, jumps, and plateaus. We propose two hybrid approaches that use both direct and FFA-based optimization and find that they perform well. All FFA-based algorithms also perform better on satisfiability problems than any of the pure algorithm variants