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Welcome to the forefront of transformative research that converges complex systems, multi-agent systems, evolutionary computation, swarm intelligence, granular computing, information granulation, and representation into a groundbreaking algorithm – Evolutive Agent-Based Clustering Classifier (E-ABC). This manifesto heralds the dawn of a new era in data analysis, where the union of evolutionary learning and localized metrics redefine clustering and classification paradigms.

E-ABC emerges as an innovative agent-based algorithm, where each agent executes a simple clustering procedure on a localized sub-sample of the dataset. The evolutionary orchestration of these agents by a genetic algorithm unveils a set of well-formed clusters, illuminating hidden patterns and recurrences in the data. Unlike conventional clustering algorithms, E-ABC transcends global metrics, adopting localized weights in the dissimilarity measure.

Intricately tied to the concept of "locality", E-ABC ensures that the dissimilarity measure is valid in the region around cluster representatives. Through this approach, the evolutionary clustering process identifies the most salient subspace where clusters manifest, uncovering crucial insights for understanding complex systems and multi-agent interactions.

Three fundamental concepts stand at the core of E-ABC's prowess. Firstly, it empowers evolutionary learning to thrive on localized metrics, liberating the algorithm from the constraints of global feature selection procedures. This enables E-ABC to discern which features are essential in characterizing distinct input space regions, laying the foundation for classification tasks that transcend conventional paradigms.

Secondly, E-ABC harnesses the wisdom of swarm intelligence, epitomizing the collective intelligence of agents that collaboratively explore and converge towards optimal clustering solutions. The synergy of agents orchestrates the emergence of coherent clusters, breathing life into data-driven insights that shape diverse applications.

Lastly, E-ABC delves into the realm of granular computing and information granulation. By capturing data patterns at different granular levels, the algorithm gains the unique capability to tackle complex, high-dimensional datasets while preserving essential information. This granular approach imbues E-ABC with interpretability and scalability, enhancing its applicability across diverse domains.

Our manifesto invites researchers, engineers, and visionaries to unite on this transformative journey. Embrace the potential of E-ABC to revolutionize clustering and classification, unveiling the intricate dynamics of complex systems and multi-agent interactions. Together, let us pioneer a future where localized metrics and evolutionary learning converge, shaping a world where data-driven insights empower us to navigate and understand the complexity of our interconnected world.