Journal of Bio-Inspired Computation Research (JBICR)

Aim and Scope: The Journal of Bio-Inspired Computation Research (JBICR) is an international research journal, which publishes articles describing recent fundamental contributions in the field of bio-inspired computation. Biologically-inspired computation (bio-inspired computation) is a field of study that brings together subfields related to the topics of connectionism, social behavior and emergence. It is often closely related to the field of artificial intelligence, as many of its pursuits can be linked to machine learning. It relies heavily on the fields of biology, computer science and mathematics. 
    Bio-inspired computation is the use of computers to model nature, and simultaneously the study of nature to improve the usage of computers. Biologically-inspired computing is a major subset of natural computation. 
Biology-inspired computation such as evolutionary computation, swarm intelligence bacterial foraging, cultural algorithms, neural networks, fuzzy systems, rough sets, molecular computing and other bionics is becoming increasingly important in face of the complexity of today's demanding applications. 
    Applications of this form of computation are found in diverse fields such as robotics, nanodevices, computer aided architecture, business, software engineering and a lot of other field. 
Bio-inspired computational models are recently in the focus of interest in computer science. 
    The major goal of the journal is the publication of new research results on bio-inspired computation methods and their applications.One of the main objectives of this journal is to bring together research papers from all subfields of bio-inspired computation area. The aim is to present new methods resulting from the research on this field and application of bio-inspired computation methods.

Submission: Authors are requested to submit their papers electronically to submit@ripublication.com with mention journal title (JBICR) in subject line.
 

Journal Topics

Topics of interest include (but not limited to):

Computational Intelligence

·        Evolutionary Algorithms an applications

·        Genetic algorithms 

·        Evolution strategies

·        Genetic programming

·        Evolutionary programming

·        Learning Classifiers

·        Supervised learning 

·        Unsupervised learning 

·        Neural network 

·        Support vector machines

·        Evolutionary support vector machines 

·        Fuzzy system design using evolutionary algorithms

·        Hybrid evolutionary algorithms

·        Swarm intelligence

·        Ant colony optimization

·        Multi-agent systems

·        Natural language processing
Search-Based Software Engineering

          Evolutionary Computation and Software Engineering

          Software Engineering topics include (but are not limited to),

          the application of search-based algorithms to:

·        Requirements engineering

·        System and software design

·        Implementation

·        Network design and monitoring

·        Software security

·        System and software integration

·        Quality assurance and testing

·        Project management, control, prediction, administration and

         organization

·        Maintenance, change management, optimization and

         transformation

·        Development processes

          Search-based techniques are taken to include (but are not limited

          to):

·        Genetic Algorithms

·        Genetic Programming

·        Evolution Strategies

·        Evolutionary Programming

·        Simulated Annealing

·        Tabu Search

·        Ant Colony Optimization

·        Particle Swarm Optimization

Applications:

·    Multiobjective optimization applications

·    Financial modeling

·    Intrusion detection and cryptography

·    NP hard problems 

·    Bioinformatics

·    Data mining

·    Knowledge management

·    Natural language processing

·    Image processing

·    Nonlinear network problems

·    Planning and scheduling

·    Brain-computer interface technologies

·    Evolutionary economy

·   Computational engineering 

 

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