This paper introduces a novel improved evolutionary algorithm, which combines genetic algorithms and hill climbing. Genetic Algorithms (GA) belong to a class of well established optimization meta-heuristics and their behavior are studied and analyzed in great detail. Various modifications were proposed by different researchers, for example modifications to the mutation operator. These modifications usually change the overall behavior of the algorithm. This paper presents a binary GA with a modified mutation operator, which is based on the well-known Hill Climbing Algorithm (HCA). The resulting algorithm, referred to as GAHC, also uses an elite tournament selection operator. This selection operator preserves the best individual from the GA population during the selection process while maintaining the positive characteristics of the standard tournament selection. This paper discusses the GAHC algorithm and compares its performance wit! h standard GA.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Matousek, Radomil; Minar, Petr: Stabilization of chaotic logistic equation using HC12 and grammatical evolution (2013)
- Oplatkova, Zuzana Kominkova; Senkerik, Roman: Evolutionary synthesis of complex structures -- pseudo neural networks for the task of iris dataset classification (2013)
- Skanderova, Lenka; Zelinka, Ivan; Šaloun, Petr: Chaos powered selected evolutionary algorithms (2013) ioport
- Zelinka, Ivan; Chadli, Mohammed; Davendra, Donald; Senkerik, Roman; Pluhacek, Michal; Lampinen, Jouni: Do evolutionary algorithms indeed require random numbers? Extended study (2013)
- Matousek, Radomil; Zampachova, Eva: Promising GAHC and HC12 algorithms in global optimization tasks (2011)
- Matousek, Radomil: GAHC: Hybrid genetic algorithm (2009)