Neuroevolution with Analog Genetic Encoding

The evolution of artificial neural networks (ANNs) is often used to tackle difficult control problems. There are different approaches to the encoding of neural networks in artificial genomes. Analog Genetic Encoding (AGE) is a new implicit method derived from the observation of biological genetic regulatory networks. This paper shows how AGE can be used to simultaneously evolve the topology and the weights of ANNs for complex control systems. AGE is applied to a standard benchmark problem and we show that its performance is equivalent or superior to some of the most powerful algorithms for neuroevolution in the literature.

The Age of Analog Networks

Peter Dürr, Dario Floreano, Daniel Marbach, Claudio Mattiussi

A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamical devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Analog networks are typically complex systems which include nonlinear feedback loops and possess temporal dynamics at different timescales. When tackled by a human expert both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. In this paper we will discuss the general relevance of the analog network concept and describe an evolutionary approach to the automatic synthesis and reverse engineering of analog networks. The proposed approach is called analog genetic encoding (AGE) and realizes an implicit genetic encoding of analog networks. AGE permits the evolution of human-competitive solutions to real-world analog network design and identification problems. This is illustrated by some examples of application to the design of electronic circuits, control systems, learning neural architectures, and to the reverse engineering of biological networks.

2008

Evolutionary synthesis of analog networks

Claudio Mattiussi

The significant increase in the available computational power that took place in recent decades has been accompanied by a growing interest in the application of the evolutionary approach to the synthesis of many kinds of systems and, in particular, to the synthesis of systems like analog electronic circuits, neural networks, and, more generally, autonomous systems, for which no satisfying systematic and general design methodology has been found to date. Despite some interesting results in the evolutionary synthesis of these kinds of systems, the endowment of an artificial evolutionary process with the potential for an appreciable increase of complexity of the systems thus generated appears still an open issue. In this thesis the problem of the evolutionary growth of complexity is addressed taking as starting point the insights contained in the published material reporting the unfinished work done in the late 1940s and early 1950s by John von Neumann on the theory of self-reproducing automata. The evolutionary complexity-growth conditions suggested in that work are complemented here with a series of auxiliary conditions inspired by what has been discovered since then relatively to the structure of biological systems, with a particular emphasis on the workings of genetic regulatory networks seen as the most elementary, full-fledged level of organization of existing living organisms. In this perspective, the first chapter is devoted to the formulation of the problem of the evolutionary growth of complexity, going from the description of von Neumann's complexity-growth conditions to the specification of a set of auxiliary complexity-growth conditions derived from the analysis of the operation of genetic regulatory networks. This leads to the definition of a particular structure for the kind of systems that will be evolved and to the specification of the genetic representation for them. A system with the required structure — for which the name analog network is suggested — corresponds to a collection of devices whose terminals are connected by links characterized by a scalar value of interaction strength. One of the specificities of the evolutionary system defined in this thesis is the way these values of interaction strength are determined. This is done by associating with each device terminal of the evolving analog network a sequence of characters extracted from the sequences that constitute the genome representing the network, and by defining a map from pairs of sequences of characters to values of interaction strength. Whereas the first chapter gives general prescriptions for the definition of an evolutionary system endowed with the desired complexity-growth potential, the second chapter is devoted to the specification of all the details of an actual implementation of those prescriptions. In this chapter the structure of the genome and of the corresponding genetic operators are defined. A technique for the genetic encoding of the devices constituting the analog network is described, along with a way to implement the map that specifies the interaction between the devices of the evolved system, and between them and the devices constituting the external environment of the evolved system. The proposed implementation of the interaction map is based on the local alignment of sequences of characters. It is shown how the parameters defining the local alignment can be chosen, and what strategies can be adopted to prevent the proliferation of unwanted interactions. The third chapter is devoted to the application of the evolutionary system defined in the second chapter to problems aimed at assessing the suitability in an evolutionary context of the local alignment technique and to problems aimed at assessing the evolutionary potential of the complete evolutionary system when applied to the synthesis of analog networks. Finally, the fourth chapter briefly considers some further questions that are relevant to the proposed approach but could not be addressed in the context of this thesis. A series of appendixes is devoted to some complementary issues: the definition of a measure of diversity for an evolutionary population employing the genetic description introduced in this thesis; the choice of the quantizer for the values of interaction strength between the devices constituting the evolved analog network; the modifications required to use the analog electronic circuit simulator SPICE as a simulation engine for an evolutionary or an optimization process.

EPFL2005

Analog Genetic Encoding for the Evolution of Circuits and Networks

Dario Floreano, Claudio Mattiussi

This paper describes a new kind of genetic representation called analog genetic encoding (AGE). The representation is aimed at the evolutionary synthesis and reverse engineering of circuits and networks such as analog electronic circuits, neural networks, and genetic regulatory networks. AGE permits the simultaneous evolution of the topology and sizing of the networks. The establishment of the links between the devices that form the network is based on an implicit definition of the interaction between different parts of the genome. This reduces the amount of information that must be carried by the genome relatively to a direct encoding of the links. The application of AGE is illustrated with examples of analog electronic circuit and neural network synthesis. The performance of the representation and the quality of the results obtained with AGE are compared with those produced by genetic programming.

2007

Evolutionary synthesis of analog networks

Claudio Mattiussi

EPFL2005