Polymorphy and Hybridization in Genetically Programmed Networks

. In this paper we discuss the polymorphic abilities of a new distributed representation for genetic programming, called Genetically Programmed Networks. These are inspired in a common structure in natural complex adaptive systems, where system functionality frequently emerges from the combined functionality of simple computational entities, densely interconnected for information exchange. A Genetically Programmed Network can be evolved into a distributed program, a rule based system or a neural network with simple adjustments to the evolutionary algorithm. The space of possible network topologies can also be easily controlled. This allows the fast exploration of various search spaces thus increasing the possibility of finding a (or a better) solution. Experimental results are presented to support our claims. 1. Introduction As artificial agents grow in intelligence and autonomy it becomes harder to design by hand the controllers to guide those agents. Evolutionary approach..

In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

This research presents In situ Distributed Genetic Programming (IDGP) as a framework for distributively evolving logic while attempting to maintain acceptable average performance on highly resource-constrained embedded networked devices. The framework is motivated by the proliferation of devices employing microcontrollers with communications capability and the absence of online learning approaches that can evolve programs for them. Swarm robotics, Internet of Things (IoT) devices including smart phones, and arguably the most constrained of the embedded systems, Wireless Sensor Networks (WSN) motes, all possess the capabilities necessary for the distributed evolution of logic - specifically the abilities of sensing, computing, actuation and communications. Genetic programming (GP) is a mechanism that can evolve logic for these devices using their “native” logic representation (i.e. programs) and so technically GP could evolve any behaviour that can be coded on the device. IDGP is designed, implemented, demonstrated and analysed as a framework for evolving logic via genetic programming on highly resource-constrained networked devices in real-world environments while achieving acceptable average performance. Designed with highly resource-constrained devices in mind, IDGP provides a guide for those wishing to implement genetic programming on such systems. Furthermore, an implementation on mote class devices is demonstrated to evolve logic for a time-varying sense-compute-act problem and another problem requiring the evolution of primitive communications. Distributed evolution of logic is also achieved by employing the Island Model architecture, and a comparison of individual and distributed evolution (with the same and slightly different goals) presented. This demonstrates the advantage of leveraging the fact that such devices often reside within networks of devices experiencing similar conditions. Since GP is a population-based metaheuristic which relies on the diversity of the population to achieve learning, many, if not most, programs within the population exhibit poor performance. As such, the average observed performance (pool fitness) of the population using the standard GP learning mechanism is unlikely to be acceptable for online learning scenarios. This is suspected to be the reason why no previous attempts have been made to deploy standard GP as an online learning approach. Nonetheless, the benefits of GP for evolving logic on such devices are compelling and motivated the design of a novel satisficing heuristic called Fitness Importance (FI). FI is population-based heuristic used to bias the evaluation of candidate solutions such that an “acceptable” average fitness (AAF) is achieved while also achieving ongoing, though diminished, learning capacity. This trade off motivated further investigation into whether dynamically adjusting the average performance in response to AAF would be superior to a constant, balanced, performing-learning approach. Dynamic and constant strategies were compared on a simple problem where the AAF target was changed during evolution, revealing that dynamically tracking the AAF target can yield a higher success rate in meeting the AAF. The combination of IDGP and FI offers a novel approach for achieving online learning with GP on highly resource-constrained embedded systems. Furthermore, it simultaneously considers the acceptable average performance of the system which may change during the operational lifetime. This approach could be applied to swarm and cooperative robot systems, WSN motes or IoT devices allowing them to cooperatively learn and adapt their logic locally to meet dynamic performance requirements