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Abstract-For a mobile robot to move in a known environment and operate successfully, first it needs to robustly determine its initial position and orientation relative to the map, and then update its position while moving in the environment. Thus determining robot's position is one of the most important tasks in mobile robotics. This task consists of "global localization" and "robot's pose tracking". In this paper two recent sample-based evolutionary methods for globally localizing the position of a mobile robot are proposed. The first method is a modified version of genetic algorithm called Differential Evolution (DE) which is based on natural selection. The second one is Particle Swarm Optimization (PSO) which is based on bird flocking. DE evaluates initial population using the probabilistic motion and observation models and the evolution of the individuals is performed by evolutionary operators. PSO adjusts the velocity and location of particles towards target (robot's pose) through a problem space on the basis of information about each particle's previous best location and the best previous location of its neighbors. Our results illustrate the excellence of these two methods over standard Monte Carlo localization algorithm with regard to convergence rate, speed and computational cost.

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A framework is introduced for applying GP to streaming data classification tasks under label budgets. This is a fundamental requirement if GP is going to adapt to the challenge of streaming data environments. The framework proposes three elements: a sampling policy, a data subset and a data archiving policy. The sampling policy establishes on what basis data is sampled from the stream, and therefore when label information is requested. The data subset is used to define what GP individuals evolve against. The composition of such a subset is a mixture of data forwarded under the sampling policy and historical data identified through the data archiving policy. The combination of sampling policy and the data subset achieve a decoupling between the rate at which the stream passes and the rate at which evolution commences. Benchmarking is performed on two artificial data sets with specific forms of sudden shift and gradual drift as well as a well known real-world data set.

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