Evolutionary Intelligence - Springer | 2024 Impact Factor: 2.6 | Cite Score:7.4 | Q2

Cite Score and Journal Rank of Evolutionary Intelligence

• About: The Evolutionary Intelligence journal is a peer-reviewed publication dedicated to the study and application of evolutionary algorithms and techniques in the field of artificial intelligence. It serves as a platform for presenting research on the development and application of evolutionary methods to solve complex problems in various domains.
• Objective:
  The primary objective of the journal is to advance the field of evolutionary intelligence by publishing high-quality research that explores new algorithms, techniques, and applications of evolutionary approaches. It aims to foster innovation and provide insights into how evolutionary methods can be applied to address challenges in artificial intelligence and beyond.
• Topics Covered:
  The Evolutionary Intelligence journal covers a broad range of topics including: Evolutionary algorithms and strategies Genetic algorithms and programming Evolutionary computation Swarm intelligence and optimization Multi-objective optimization Hybrid evolutionary approaches Applications of evolutionary intelligence in various domains Theoretical foundations and developments Benchmarking and performance evaluation of evolutionary methods Case studies and real-world implementations
• Impact:
  The journal has a significant impact by contributing to the advancement of evolutionary intelligence techniques and their applications. Its publications offer valuable insights and developments that help researchers and practitioners apply evolutionary methods to solve complex problems and improve system performance.
• Significance:
  The Evolutionary Intelligence journal is significant in advancing the field of artificial intelligence by focusing on evolutionary approaches. By providing a platform for high-quality research, the journal supports the growth of knowledge and innovation in evolutionary methods, helping to address various challenges and applications in AI.

• Journal Home:  Journal Homepage

• Editor-in-Chief:  Vincenzo Loia

• Scope: Evolutionary Intelligence is a journal that focuses on the study of evolutionary approaches in intelligence systems and computational models. Here are the key areas typically covered:
• 1. Evolutionary Algorithms
  Research on evolutionary algorithms such as genetic algorithms, evolutionary programming, and evolutionary strategies
  Studies on the development and application of these algorithms to various problems
  Development of new evolutionary techniques and improvements to existing methods
• 2. Evolutionary Computation
  Research on computational methods inspired by evolutionary processes
  Studies on the application of evolutionary computation in optimization, machine learning, and data analysis
  Development of novel computational models and frameworks based on evolutionary principles
• 3. Adaptive Systems
  Research on adaptive systems that evolve and improve over time
  Studies on self-organizing and self-adaptive systems in various domains
  Development of methods for enhancing the adaptability and resilience of systems
• 4. Genetic Programming
  Research on genetic programming techniques and their applications
  Studies on the evolution of computer programs and algorithms
  Development of new genetic programming methodologies and their practical applications
• 5. Evolutionary Robotics
  Research on the application of evolutionary techniques in robotics
  Studies on evolving robotic behaviors, designs, and control systems
  Development of evolutionary methods for improving robot functionality and performance
• 6. Evolutionary Machine Learning
  Research on the integration of evolutionary algorithms with machine learning techniques
  Studies on the use of evolutionary approaches to enhance machine learning models
  Development of new hybrid models that leverage both evolutionary and learning-based methods
• 7. Multi-Objective Optimization
  Research on evolutionary approaches to solving multi-objective optimization problems
  Studies on techniques for balancing and optimizing multiple objectives simultaneously
  Development of methods for efficiently exploring trade-offs in multi-objective scenarios
• 8. Evolutionary Systems Design
  Research on the design of systems and solutions based on evolutionary principles
  Studies on the application of evolutionary methods to system architecture and design
  Development of innovative designs and solutions inspired by evolutionary processes
• 9. Computational Biology and Bioinformatics
  Research on the use of evolutionary techniques in computational biology and bioinformatics
  Studies on evolutionary models for understanding biological systems and processes
  Development of bioinformatics tools and methods based on evolutionary principles
• 10. Theoretical and Experimental Studies
  Research on the theoretical foundations of evolutionary intelligence
  Studies on experimental validation and benchmarking of evolutionary methods
  Development of frameworks for evaluating and comparing evolutionary algorithms and techniques
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• Print ISSN:  1864-5909

  Electronic ISSN:  1864-5917

• Abstracting and Indexing:  Scopus

• Imapct Factor 2024:  2.6

• Subject Area and Category:   Computer Science, Artificial Intelligence, Computer Vision and Pattern Recognition, Mathematics, Mathematics (miscellaneous), Neuroscience, Cognitive Neuroscience

• Publication Frequency:  

• H Index:  44

• Best Quartile:

  Q1:  

  Q2:  Computer Vision and Pattern Recognition

  Q3:  

  Q4:  

• Cite Score:  7.4

• SNIP:  1.085

• Journal Rank(SJR):  0.609

• Latest Articles:   Latest Articles in Evolutionary Intelligence

• Guidelines for Authors: Evolutionary Intelligence Author Guidelines

• Paper Submissions: Paper Submissions in Evolutionary Intelligence

• Publisher:  Springer Verlag

  Country:  Germany

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