Cosmic Swarms: A search for Supermassive Black Holes in the LISA data stream with a Hybrid Evolutionary Algorithm

We describe a hybrid evolutionary algorithm that can simultaneously search for multiple supermassive black hole binary (SMBHB) inspirals in LISA data. The algorithm mixes evolutionary computation, Metropolis-Hastings methods and Nested Sampling. The inspiral of SMBHBs presents an interesting problem for gravitational wave data analysis since, due to the LISA response function, the sources have a bi-modal sky solution. We show here that it is possible not only to detect multiple SMBHBs in the data stream, but also to investigate simultaneously all the various modes of the global solution. In all cases, the algorithm returns parameter determinations within $5\sigma$ (as estimated from the Fisher Matrix) of the true answer, for both the actual and antipodal sky solutions.Comment: submitted to Classical & Quantum Gravity. 19 pages, 4 figure

Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization

A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). The fitness landscape of human subjective evaluation in IEC is very difficult and impossible to model, even with a hypothesis of what its definition might be. In this paper, we propose a method to establish a human model in projected high dimensional search space by kernel classification for enhancing IEC search. Because bivalent logic is a simplest perceptual paradigm, the human model is established by considering this paradigm principle. In feature space, we design a linear classifier as a human model to obtain user preference knowledge, which cannot be supported linearly in original discrete search space. The human model is established by this method for predicting potential perceptual knowledge of human. With the human model, we design an evolution control method to enhance IEC search. From experimental evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search significantly

Local Fitness Landscape Exploration Based Genetic Algorithms

Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm" (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems

A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

The rapid development of earth observation technology has produced large quantities of remote-sensing data. Unsupervised classification (i.e. clustering) of remote-sensing images, an important means to acquire land-use/cover information, has become increasingly in demand due to its simplicity and ease of application. Traditional methods, such as k-means, struggle to solve this NP-hard (Non-deterministic Polynomial hard) image classification problem. Particle swarm optimization (PSO), always achieving better result than k-means, has recently been applied to unsupervised image classification. However, PSO was also found to be easily trapped on local optima. This article proposes a novel unsupervised Levy flight particle swarm optimization (ULPSO) method for image classification with balanced exploitation and exploration capabilities. It benefits from a new searching strategy: the worst particle in the swarm is targeted and its position is updated with Levy flight at each iteration. The effectiveness of the proposed method was tested with three types of remote-sensing imagery (Landsat Thematic Mapper (TM), Flightline C1 (FLC), and QuickBird) that are distinct in terms of spatial and spectral resolution and landscape. Our results showed that ULPSO is able to achieve significantly better and more stable classification results than k-means and the other two intelligent methods based on genetic algorithm (GA) and particle swarm optimization (PSO) over all of the experiments. ULPSO is, therefore, recommended as an effective alternative for unsupervised remote-sensing image classification

Evolutionary Legged Robotics

Due to the technological advance, robotic systems become more and more interesting for industrial and home applications. Popular examples are given by robotic lawn mower, robot vacuum cleaner, and package drones. Beside the toy industry, legged robots are not as popular, although they have some clear advantages compared to wheeled systems. With their flexibility concerning the locomotion, they are able to adapt their walking pattern to different environments. For instance they can walk over obstacles and gaps or climb over rubble and stairs. Another possible advantage could be a redundancy for locomotion. A faulty motor in one limb could be compensated by other motors in the kinematic chain. As well, multiple failing legs can be compensated by an adapted walking pattern. Compared to this, the more complex mechatronic systems represent a major challenge to the construction and the control. This thesis is dedicated to the control of complex walking robots. Genetic algorithms are applied to generate walking patterns for different robots. The evolutionary development of walking patterns is done in a simulation software. Results of various approaches are transferred and tested on existing systems which have been developed at RIC/DFKI. Different robotic systems are used to evaluate the generality of the applied methods. Eventually, a method is developed that can be utilized, with a few system specific modifications, for a variety of legged robots. As basis for the development and investigation of several methods, software tools are designed to generalize the application of applying genetic algorithms to legged locomotion. These tools include a simulation environment, a behavior representation, a genetic algorithm and a learning and benchmark framework. The simulation environment is adapted to the behavior of real robotic systems via reference experiments. In addition, the simulation is extended by a foot contact model for loose surfaces. The evaluation of the genetic algorithm is done on several benchmark problems and compared to three existing algorithms. This thesis contributes to the state of the art in many areas. The developed methodology can easily be applied to several complex robotic systems due to its transferability. The genetic algorithm and the hierarchical behavior representation provide a new opportunity to control the generation of the offspring in an evolutionary process. In addition, the developed software tools are an important contribution for their respective research fields