26,899 research outputs found
Integrating Evolutionary Computation with Neural Networks
There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique
Evolution and development of Brain Networks: From Caenorhabditis elegans to Homo sapiens
Neural networks show a progressive increase in complexity during the time
course of evolution. From diffuse nerve nets in Cnidaria to modular,
hierarchical systems in macaque and humans, there is a gradual shift from
simple processes involving a limited amount of tasks and modalities to complex
functional and behavioral processing integrating different kinds of information
from highly specialized tissue. However, studies in a range of species suggest
that fundamental similarities, in spatial and topological features as well as
in developmental mechanisms for network formation, are retained across
evolution. 'Small-world' topology and highly connected regions (hubs) are
prevalent across the evolutionary scale, ensuring efficient processing and
resilience to internal (e.g. lesions) and external (e.g. environment) changes.
Furthermore, in most species, even the establishment of hubs, long-range
connections linking distant components, and a modular organization, relies on
similar mechanisms. In conclusion, evolutionary divergence leads to greater
complexity while following essential developmental constraints
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
While neuroevolution (evolving neural networks) has a successful track record
across a variety of domains from reinforcement learning to artificial life, it
is rarely applied to large, deep neural networks. A central reason is that
while random mutation generally works in low dimensions, a random perturbation
of thousands or millions of weights is likely to break existing functionality,
providing no learning signal even if some individual weight changes were
beneficial. This paper proposes a solution by introducing a family of safe
mutation (SM) operators that aim within the mutation operator itself to find a
degree of change that does not alter network behavior too much, but still
facilitates exploration. Importantly, these SM operators do not require any
additional interactions with the environment. The most effective SM variant
capitalizes on the intriguing opportunity to scale the degree of mutation of
each individual weight according to the sensitivity of the network's outputs to
that weight, which requires computing the gradient of outputs with respect to
the weights (instead of the gradient of error, as in conventional deep
learning). This safe mutation through gradients (SM-G) operator dramatically
increases the ability of a simple genetic algorithm-based neuroevolution method
to find solutions in high-dimensional domains that require deep and/or
recurrent neural networks (which tend to be particularly brittle to mutation),
including domains that require processing raw pixels. By improving our ability
to evolve deep neural networks, this new safer approach to mutation expands the
scope of domains amenable to neuroevolution
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
Surrogate-assistance approaches have long been used in computationally
expensive domains to improve the data-efficiency of optimization algorithms.
Neuroevolution, however, has so far resisted the application of these
techniques because it requires the surrogate model to make fitness predictions
based on variable topologies, instead of a vector of parameters. Our main
insight is that we can sidestep this problem by using kernel-based surrogate
models, which require only the definition of a distance measure between
individuals. Our second insight is that the well-established Neuroevolution of
Augmenting Topologies (NEAT) algorithm provides a computationally efficient
distance measure between dissimilar networks in the form of "compatibility
distance", initially designed to maintain topological diversity. Combining
these two ideas, we introduce a surrogate-assisted neuroevolution algorithm
that combines NEAT and a surrogate model built using a compatibility distance
kernel. We demonstrate the data-efficiency of this new algorithm on the low
dimensional cart-pole swing-up problem, as well as the higher dimensional
half-cheetah running task. In both tasks the surrogate-assisted variant
achieves the same or better results with several times fewer function
evaluations as the original NEAT.Comment: In GECCO 201
A Review on Biological Inspired Computation in Cryptology
Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
- âŠ