Memetic Computing

Scope & Guideline

Navigating the Future of Computer Science

Introduction

Immerse yourself in the scholarly insights of Memetic Computing with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN1865-9284
PublisherSPRINGER HEIDELBERG
Support Open AccessNo
CountryGermany
TypeJournal
Convergefrom 2009 to 2024
AbbreviationMEMET COMPUT / Memet. Comput.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY

Aims and Scopes

Memetic Computing focuses on the intersection of genetic algorithms, evolutionary strategies, and their application in solving complex optimization problems across various domains. The journal aims to foster interdisciplinary research that combines computational intelligence with real-world applications.
  1. Evolutionary Algorithms and Optimization Techniques:
    The journal emphasizes the development and application of evolutionary algorithms, including genetic algorithms, memetic algorithms, and hybrid approaches, to solve complex optimization problems.
  2. Multi-Objective Optimization:
    A core focus is on multi-objective optimization, where solutions must satisfy multiple criteria, reflecting real-world complexities in fields such as engineering, logistics, and finance.
  3. Applications in Various Domains:
    Research published in the journal spans diverse fields, including bioinformatics, robotics, telecommunications, and resource management, showcasing the versatility of memetic computing approaches.
  4. Integration with Machine Learning:
    The journal explores the integration of evolutionary strategies with machine learning techniques, particularly in enhancing learning algorithms and improving decision-making processes.
  5. Dynamic and Adaptive Systems:
    There is a consistent interest in dynamic optimization problems where environments change over time, requiring algorithms that can adapt and respond effectively.
Recent publications in Memetic Computing indicate several emerging themes that reflect the journal's adaptation to current technological advancements and research needs.
  1. Deep Reinforcement Learning Integration:
    There is a rising trend in integrating deep reinforcement learning with evolutionary strategies, indicating a growing interest in leveraging the strengths of both fields to tackle complex decision-making problems.
  2. Adaptive and Self-Adjusting Algorithms:
    Emerging research focuses on the development of algorithms that can adapt their parameters and strategies in real-time, which is crucial for applications in dynamic environments.
  3. Graph-Based Learning Techniques:
    The use of graph structures in optimization problems is gaining traction, particularly in applications related to network routing, data representation, and complex systems modeling.
  4. Surrogate-Assisted Optimization:
    Surrogate models are increasingly being utilized to enhance the efficiency of optimization processes, especially in scenarios where evaluations are costly or time-consuming.
  5. Emotion-Aware and Bio-Inspired Algorithms:
    Emerging themes include bio-inspired algorithms that incorporate emotional and cognitive elements, reflecting a trend towards more human-like decision-making processes in computational models.

Declining or Waning

While Memetic Computing has a broad range of research areas, certain themes have shown a decline in focus over the recent years, indicating a shift in the research landscape.
  1. Traditional Genetic Algorithms:
    There appears to be a waning interest in traditional genetic algorithms as standalone techniques, with a shift towards more hybrid and adaptive approaches that incorporate other methodologies.
  2. Static Optimization Problems:
    Research addressing static optimization problems is becoming less prominent, likely due to the growing complexity of real-world applications that require dynamic and adaptable solutions.
  3. Single-Objective Optimization:
    The focus on single-objective optimization scenarios is declining as researchers increasingly recognize the necessity of considering multiple objectives and constraints in practical applications.
  4. Basic Heuristic Techniques:
    Basic heuristic techniques are less frequently explored, as the field moves towards more sophisticated and integrated approaches that combine heuristics with machine learning and other advanced methods.

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