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Evolutionary computation (EC) in Java has long been dominated by legacy frameworks (ECJ, Jenes, Watchmaker) that, while robust, were designed before the widespread adoption of lambdas, streams, modern concurrency, and value types (Project Valhalla). This paper introduces – a set of idiomatic, high-performance patterns for implementing evolutionary algorithms (EAs) that leverage Java 21+ features. We demonstrate that by rethinking representation, variation, and selection through functional paradigms and structured concurrency, one can achieve a 3–5x reduction in code complexity and up to 10x improvement in throughput for large-population problems. We provide a reference implementation and benchmarks on symbolic regression and multi-objective knapsack. The results suggest that “best” EvoJav is not a single library, but a mindset shift: toward immutable genomes, parallel streams for fitness evaluation, and virtual threads for island models.

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EvoJ excels at optimizing mathematical functions. In its basic tutorials, it solves the problem of finding the minimum of a function like Z(x, y) = 12*x^2 + 8*x + 9*y^2 . By declaring an interface with @Range annotations for x and y and providing a fitness function that returns a value inversely related to Z(x, y) , EvoJ efficiently evolves towards the global minimum. Evolutionary computation (EC) in Java has long been

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