Genetic Algorithms (GA) have proven to be versatile tools for solving optimization problems in various domains. However, traditional reliance on fixed-size populations can impose constraints on storage, transmission, and adaptability, particularly in resource-limited or distributed systems. This paper introduces the Condensed Population Genetic Algorithm (CPGA), a novel approach that replaces traditional population storage with an integer-array representation, enabling efficient condensation and reconstruction of populations. By aggregating the population into a single integer array of bit-counts rather than a floating-point probability vector, the CPGA retains exact population statistics while drastically reducing its memory footprint. Compared to probabilistic model–based methods such as the Compact Genetic Algorithm (cGA) or Estimation of Distribution Algorithms (EDA), which require full floating-point probability distributions and often complex parameter updates, our integer-array scheme leverages simple, exact count updates. Through controlled modification and regeneration of individuals, the CPGA demonstrates improved scalability and adaptability, especially in distributed and embodied evolutionary systems. Benchmark results on the BBOB-17 suite show the CPGA matches or exceeds the performance of traditional GAs while offering significant gains in storage efficiency and communication overhead; experiments in the Atari ALE framework further validate its efficacy across disparate domains.