Complexity-based speciation and genotype representation for neuroevolution
This paper introduces a speciation principle for neuroevolution where evolving networks are grouped into species based on the number of hidden neurons, which is indicative of the complexity of the search space. This speciation principle is indivisibly coupled with a novel genotype representation which is characterised by zero genome redundancy, high resilience to bloat, explicit marking of recurrent connections, as well as an efficient and reproducible stack-based evaluation procedure for networks with arbitrary topology. Furthermore, the proposed speciation principle is employed in several techniques designed to promote and preserve diversity within species and in the ecosystem as a whole. The competitive performance of the proposed framework, named Cortex, is demonstrated through experiments. A highly customisable software platform which implements the concepts proposed in this study is also introduced in the hope that it will serve as a useful and reliable tool for experimentation in the field of neuroevolution.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversitySimilar Papers 제목 키워드 기반
Improving Neuroevolution Using Island Extinction and Repopulation
Neuroevolution commonly uses speciation strategies to better explore the search space of neural network architectures. One such speciation strategy is through the use of islands, which are also popular in improving perfo…
Evolutionary AlgorithmsTime SeriesTime Series AnalysisNeuroevolution with Perceptron Turing Machines
We introduce the perceptron Turing machine and show how it can be used to create a system of neuroevolution. Advantages of this approach include automatic scaling of solutions to larger problem sizes, the ability to expe…
On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent Search
Evolvability refers to the ability of an individual genotype (solution) to produce offspring with mutually diverse phenotypes. Recent research has demonstrated that divergent search methods, particularly novelty search, …
DiversityA Self-adaptive Neuroevolution Approach to Constructing Deep Neural Network Architectures Across Different Types
Neuroevolution has greatly promoted Deep Neural Network (DNN) architecture design and its applications, while there is a lack of methods available across different DNN types concerning both their scale and performance. I…
Generative Adversarial NetworkEvolving Neural Networks through Augmenting Topologies
An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the b…
Neural Architecture SearchReinforcement Learning