Tüze Kuyucu

Evolutionary optimization of pheromone-based stigmergic communication

Pheromone-based stigmergic communication is well suited for the coordination of swarm of robots in the exploration of unknown areas. We introduce a guided probabilistic exploration of an unknown environment by combining random movement and stigmergic guidance. Pheromone-based stigmergic communication among simple entities features various complexities that have significant effects on the overall swarm coordination, but are poorly understood. We propose a genetic algorithm for the optimization of parameters related to pheromone-based stigmergic communication. As a result, we achieve human-competitive tuning and obtain a better understanding of these parameters.

A model for intrinsic artificial development featuring structural feedback and emergent growth

A model for intrinsic artificial development is introduced in this paper. The proposed model features a novel mechanism where growth emerges, rather than being triggered by a single action. Different types of cell signalling ensure that breaking symmetries is rather the norm than an exception, and gene activity is regulated on two layers: first, by the proteins that are produced by the gene regulatory network (GRN). Second, through structural feedback by second messenger molecules, which are not directly produced through gene expression, but are produced by sensor proteins, which take the cells structure into account. The latter feedback mechanism is a novel approach, intended to enable adaptivity and environment coupling in real-world applications. The model is implemented in hardware, and is designed to run autonomously in resource limited embedded systems. Initial experiments are carried out to measure long-term stability, dynamics, adaptivity and scalability of the new approach. Furthermore the ability of the GRN to produce patterns of different symmetries is examined.

Fitness functions for the unconstrained evolution of digital circuits

This work is part of a project that aims to develop and operate integrated evolvable hardware systems using unconstrained evolution. Experiments are carried out on an evolvable hardware platform featuring both combinatorial and registered logic as well as sequential feedback loops. In order to be able to accurately assess the transient output of the system and at the same time speed up evolution, new fitness evaluation methods are introduced. These bitwise and hierarchical fitness evaluation methods are adapted and further developed specifically for hardware implementation. It is shown that the newly developed approaches are particularly powerful in coping with two important issues: computational ambiguities, which generally occur when evaluating binary strings, and transient effects resulting from measuring hardware output. On two combinatorial problems it is shown that the new fitness functions improve the performance of evolution and allow stable solutions to be found more reliably. The experiments are carried out with a recently developed hardware platform called reconfigurable integrated system array (RISA).

Evolution and analysis of a robot controller based on a gene regulatory network

This paper explores the application of an artificial developmental system (ADS) to the field of evolutionary robotics by investigating the capability of a gene regulatory network (GRN) to specify a general purpose obstacle avoidance controller both in simulation and on a real robot. Experiments are carried out using the e-puck robot platform. It is further proposed to use cross-correlation between inputs and outputs in order to assess the quality of robot controllers more accurately than with observing its behaviour alone.

On the Advantages of Variable Length GRNs for the Evolution of Multicellular Developmental Systems

Biological genomes have evolved over a period of millions of years and comprise thousands of genes, even for the simplest organisms. However, in nature, only 1-2% of the genes play an active role in creating and maintaining the organism, while the majority are evolutionary fossils. This raises the question of whether a considerably larger number of (partly redundant) genes are required in order to effectively build a functional developmental system, of which, in the final system only a fraction is required for the latter to function. This paper investigates different approaches to creating artificial developmental systems (ADSs) based on variable length gene regulatory networks (GRNs). The GRNs are optimized using an evolutionary algorithm (EA). A comparison is made between the different variable length representations and fixed length representations. It is shown that variable length GRNs can achieve both reducing computational effort during optimization and increasing speed and compactness of the resulting ADS, despite the higher complexity of the encoding required. The results may also improve the understanding of how to effectively model GRN based developmental systems. Taking results of all experiments into account makes it possible to create an overall ranking of the different patterns used as a testbench in terms of their complexity. This ranking may aid to compare related work against. In addition, this allows a detailed assessment of the ADS used and enables the identification of missing mechanisms.

The Input Pattern Order Problem: Evolution of Combinatorial and Sequential Circuits in Hardware

Evolution is particularly good at finding specific solutions, which are only valid for exactly the input and environment that are presented during evolution. In most evolution experiments the input pattern order problemis not considered, even though the ability to provide a correct result for any input pattern is a prerequisite for valid circuits. Therefore, the importance of including randomness in the input pattern applied during evolution is addressed in this paper. This is shown to be mandatory--particularly in the case of unconstrained intrinsic evolution of digital circuits--in order to find valid solutions. The different ways in which unconstrained evolution and constrained evolution exploit resources of a hardware substrate are compared. It is also shown that evolution benefits from versatile input configurations. Furthermore, hierarchical fitness functions, previously introduced to improve the evolution of combinatorial circuits, are applied to the evolution of sequential circuits.

Image compression of natural images using artificial gene regulatory networks

A novel approach to image compression using a gene regulatory network (GRN) based artificial developmental system (ADS) is introduced. The proposed algorithm exploits the fact that a series of complex organisms (≡ developmental states) can be represented via a GRN description and the indices of the developmental steps in which they occur. Organisms are interpreted as tiles of an image at each developmental step which results in the (re-)construction of an image during the developmental process. It is shown that GRNs are suitable for image compression and achieve higher compression rates than JPEG when optimised for a particular image. It is also shown that the same GRN has the potential to encode multiple images, each represented by a different series of numbers of developmental steps.

On the properties of artificial development and its use in evolvable hardware

The design of a new biologically inspired artificial developmental system is described in this paper. In general, developmental systems converge slower and are more computationally expensive than direct evolution. However, the performance trends of development indicate that the full benefit of development will arise with larger and more complex problems that exhibit some sort of regularity in their structure: thus, the aim is to evolve larger electronic systems through the modularity allowed by development. The hope is that the proposed artificial developmental system will exhibit adaptivity and fault tolerance in the future. The cell signalling and the system of Gene Regulatory Networks present in biological organisms are modelled in our developmental system, and tailored for tackling real world problems on electronic hardware. For the first time, a Gene Regulatory Network system is successfully shown to develop the complete circuit structure of a desired digital circuit without the help of another mechanism or any problem specific structuring. Experiments are presented that show the modular behaviour of the developmental system, as well as its ability to solve non-modular circuit problems.

Superadditive effect of multi-robot coordination in the exploration of unknown environments via stigmergy

Abstract We propose a simple yet efficient way of coordinating multiple homogeneous robots in the exploration of unknown environments. A guided probabilistic exploration of an unknown environment is achieved via combining random movement with pheromone-based stigmergic guidance. The emergent strategy is shown to provide a scalable solution to multi-robot coordination for the area exploration task, with a faster than linear speed-up with the addition of new robots. We utilize an approach to evaluating the desired exploration behavior that emphasizes “surveying” rather than “scanning” the environment. We analyze the emergent exploration strategies and demonstrate their effectiveness in higher complexity environments.

A scalable solution to n-bit parity via artificial development

The design of electronic circuits with model-free heuristics like evolutionary algorithms is an attractive concept and field of research. Although successful to a point, evolution of circuits that are bigger than a 3-bit multiplier is hindered by the scalability problem. Modelling the biological development as an artificial genotype-phenotype mapping mechanism has been shown to improve scalability on some simple circuit problems and pattern formations. As a candidate solution to the scalability issue, an artificial developmental system is presented.