On Restricting Real-Valued Genotypes in Evolutionary Algorithms

Real-valued genotypes together with the variation operators, mutation and crossover, constitute some of the fundamental building blocks of Evolutionary Algorithms. Real-valued genotypes are utilized in a broad range of contexts, from weights in Artificial Neural Networks to parameters in robot control systems. Shared between most uses of real-valued genomes is the need for limiting the range of individual parameters to allowable bounds. In this paper we will illustrate the challenge of limiting the parameters of real-valued genomes and analyse the most promising method to properly limit these values. We utilize both empirical as well as benchmark examples to demonstrate the utility of the proposed method and through a literature review show how the insight of this paper could impact other research within the field. The proposed method requires minimal intervention from Evolutionary Algorithm practitioners and behaves well under repeated application of variation operators, leading to better theoretical properties as well as significant differences in well-known benchmarks

Evolved embodied phase coordination enables robust quadruped robot locomotion

Overcoming robotics challenges in the real world requires resilient control systems capable of handling a multitude of environments and unforeseen events. Evolutionary optimization using simulations is a promising way to automatically design such control systems, however, if the disparity between simulation and the real world becomes too large, the optimization process may result in dysfunctional real-world behaviors. In this paper, we address this challenge by considering embodied phase coordination in the evolutionary optimization of a quadruped robot controller based on central pattern generators. With this method, leg phases, and indirectly also inter-leg coordination, are influenced by sensor feedback.By comparing two very similar control systems we gain insight into how the sensory feedback approach affects the evolved parameters of the control system, and how the performances differs in simulation, in transferal to the real world, and to different real-world environments. We show that evolution enables the design of a control system with embodied phase coordination which is more complex than previously seen approaches, and that this system is capable of controlling a real-world multi-jointed quadruped robot.The approach reduces the performance discrepancy between simulation and the real world, and displays robustness towards new environments.Comment: 9 page

Detecting a Hidden Radio Frequency Transmitter in Noise based on Amplitude using Swarm Intelligence

Radio Frequency transmitters are becoming ubiquitous. For instance people oftencarry around a mobile phone containing several such transmitters. Having theability to detect and locate such transmitters in noisy environments can there-fore be very beneficial in search and rescue and disaster management. By usingamplitude measurements, simple hardware can be deployed on small and cheapUnmanned Aerial Vehicles. The use of many cooperative Unmanned Aerial Vehi-cles should enable rapid search and more precise geolocation of such transmitters.This thesis explores different search strategies to use on autonomous agentstrying to detect a hidden Radio Frequency transmitter. By testing differentSwarm Intelligence strategies important insight into the search process is gained.The experiments were run on a simulator to enable rapid testing of differentstrategies. In addition to testing baseline algorithms from the literature, a newstrategy is created, the Call-out strategy.The work done, shows that few cooperating searchers should prefer exploita-tion of the transmitted signal over exploration. However, as more agents areemployed this focus is shifted over to exploration. In addition, this work showthat several simple searchers can perform the task with high performance. Thisshows that Swarm Intelligence can be used to direct several cooperative searchersin detecting a hidden Radio Frequency transmitter

Combining MAP-Elites and Incremental Evolution to Generate Gaits for a Mammalian Quadruped Robot

Four-legged mammals are capable of showing a great variety of movement patterns, ranging from a simple walk to more complex movement such as trots and gallops. Imbuing this diversity to quadruped robots is of interest in order to improve both mobility and reach. Within the field of Evolutionary Robotics, Quality Diversity techniques have shown a remarkable ability to produce not only effective, but also highly diverse solutions. When applying this approach to four-legged robots an initial problem is to create viable movement patterns that do not fall. This difficulty stems from the challenging fitness gradient due to the mammalian morphology. In this paper we propose a solution to overcome this problem by implementing incremental evolution within the Quality Diversity framework. This allows us to evolve controllers that become more complex while at the same time utilizing the diversity produced by Quality Diversity. We show that our approach is able to generate high fitness solutions early in the search process, keep these solutions and perform a more open-ended search towards the end of evolution

On Restricting Real-Valued Genotypes in Evolutionary Algorithms

Real-valued genotypes together with the variation operators, mutation and crossover, constitute some of the fundamental building blocks of Evolutionary Algorithms. Real-valued genotypes are utilized in a broad range of contexts, from weights in Artificial Neural Networks to parameters in robot control systems. Shared between most uses of real-valued genomes is the need for limiting the range of individual parameters to allowable bounds. In this paper we will illustrate the challenge of limiting the parameters of real-valued genomes and analyse the most promising method to properly limit these values. We utilize both empirical as well as benchmark examples to demonstrate the utility of the proposed method and through a literature review show how the insight of this paper could impact other research within the field. The proposed method requires minimal intervention from Evolutionary Algorithm practitioners and behaves well under repeated application of variation operators, leading to better theoretical properties as well as significant differences in well-known benchmarks

Dynamic mutation in MAP-Elites for robotic repertoire generation

One of the core functions in most Evolutionary Algorithms is mutation. In complex search spaces, which are common in Evolutionary Robotics, mutation is often used both for optimizing existing solutions, described as exploitation, and for spanning the search space, called exploration. This presents a difficult challenge for researchers as mutation parameters must be selected with care in order to balance the two, often contradictory, effects. Strategies that vary mutation during the search often try to estimate these effects in order to modify the mutation parameters. In this regard MAP-Elites, a Quality Diversity algorithm, presents an interesting opportunity. Because factors related to exploration and exploitation are readily available during the search, optimization based on these factors could be utilized to improve the search. In this paper we study how online adaptation of mutation rate, dynamic mutation, affects MAP-Elites in order to gain insight into how the search process is affected by the mutation rate. Our study compares fixed and dynamic mutation parameters for two different complex gait controllers. The results show that dynamic mutation combines favorably with MAP-Elites and that there is a strong relation between mutation parameters and exploration that can be utilized

MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics

In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions