Krzysztof Trojanowski

Adaptive evolutionary planner/navigator for mobile robots

Based on evolutionary computation (EC) concepts, we developed an adaptive evolutionary planner/navigator (EP/N) as a novel approach to path planning and navigation. The EP/N is characterized by generality, flexibility, and adaptability. It unifies off-line planning and online planning/navigation processes in the same evolutionary algorithm which 1) accommodates different optimization criteria and changes in these criteria, 2) incorporates various types of problem-specific domain knowledge, and 3) enables good tradeoffs among near-optimality of paths, high planning efficiency, and effective handling of unknown obstacles. More importantly, the EP/N can self-tune its performance for different task environments and changes in such environments, mostly through adapting probabilities of its operators and adjusting paths constantly, even during a robots motion toward the goal.

Intelligent Information Processing and Web Mining

Derivation of new features of observed variables has two important goals: reduction of dimensionality and de-noising. A desired property of the derived new features is their meaningful interpretation. The SCoTLASS method (Jolliffe, Trendafilov and Uddin, 2003) offers such possibility. We explore the properties of the SCoTLASS method applied to the yeast genes data investigated in (Bartkowiak et al., 2003, 2004). All the derived features have really a simple meaningful structure: each new feature is spanned by two original variables belonging to the same block.

Immune-based algorithms for dynamic optimization

The main problem with biologically inspired algorithms (like evolutionary algorithms or particle swarm optimization) when applied to dynamic optimization is to force their readiness for continuous search for new optima occurring in changing locations. Immune-based algorithm, being an instance of an algorithm that adapt by innovation seem to be a perfect candidate for continuous exploration of a search space. In this paper we describe various implementations of the immune principles and we compare these instantiations on complex environments.

Adding memory to the Evolutionary Planner/Navigator

The integration of evolutionary approaches with adaptive memory processes is emerging as a promising new area for research and practical applications. In this paper, we report our study on adding memory to the Evolutionary Planner/Navigator (EP/N), which is an adaptive planning/navigation system for mobile robots based on evolutionary computation. Preliminary results from our experiments demonstrate the potential of such extension to EP/N in improving planning effectiveness in partially-known environments.

Studying Properties of Multipopulation Heuristic Approach to Non-Stationary Optimisation Tasks

Heuristic optimisation techniques, especially evolutionary algorithms were successfully applied to non-stationary optimisation tasks. One of the most important conclusions for the evolutionary approach was a three-population architecture of the algorithm, where one population plays the role of a memory while the two others are used in the searching process. In this paper the authors’ version of the three-population architecture is applied to four different heuristic algorithms. One of the algorithms is a new iterated heuristic algorithm inspired by artificial immune system and proposed by the authors. The results of experiments with a non-stationary environment showing different properties of the algorithms are presented and some general conclusions are sketched.

B-Cell Algorithm as a Parallel Approach to Optimization of Moving Peaks Benchmark Tasks

Efficiency of the B-Cell algorithm applied to one of the well-known test-case generators, the moving peaks benchmark (MPB) is a subject of study presented in this paper. We especially focused on the family of fitness landscapes generated by scenario 2 of the MPB. All of them represent the class of randomly changing environments. Some properties of the algorithm as well as the properties of the environments created by the generator MPB are discussed. A side effect of modification of one of the control parameters and its influence on the offline error measure is presented.

Searching for Memory in Artificial Immune System

In this paper an idea of the artificial immune system was used to design an algorithm for non-stationary function optimization. It was demonstrated that in the case of periodic function changes the algorithm constructively builds and uses immune memory. This result was contrasted with cases when no periodic changes occur. Further, an attempt towards the identification of optimal partitioning of the antibodies population into antibodies subjected clonal selection and programmed death of cells (apoptosis) has been done.

Evolutionary Computation: One Project, Many Directions

The field of evolutionary computation has been growing rapidly over the last few years. Yet, there are still many gaps to be filled, many experiments to be done, many questions to be answered. In this paper we examine a few important directions in which we can expect a lot of activities and significant results; we discuss them from a general perspective and in the context of a particular project: a development of an evolutionary planner/navigator in a mobile robot environment.

Properties of Quantum Particles in Multi-Swarms for Dynamic Optimization

This paper studies properties of a multi-swarm system based on a concept of physical quantum particles (mQSO). Quantum particles differ from the classic ones in the way they move. As opposed to the classic view of particle movement, where motion is controlled by linear kinematic laws, quantum particles change their location according to random distributions. The procedure for generating a new location for the quantum particle is similar to mutation operators widely used in evolutionary computation with real-valued representation. In this paper we study a set of new distributions of candidates for quantum particle location, and we show different features of these distributions. The distributions considered in this paper are divided into two classes: those with a limited range of the new location coordinates and those without such limitations. They are tested on different types of dynamic optimization problems. Experimental verification has been based on a number of testing environments and two main versions of the algorithm: with and without mechanisms protecting against stagnation caused by convergence of sub-swarms during the search process. The experimental results show the advantages of the distribution class, in which the candidates are spread out in the entire search space, and indicate the positive and negative aspects of application of anti-convergencemechanisms.

Memory Management in Artificial Immune System

In this paper an idea of the artificial immune system was used to design an algorithm for non-stationary function optimization. The unknown and varying in time optimum is treated here as an antigen and the aim of the system is to produce antibodies. Three different strategies awarding memory cells are investigated.