On the heritability of dandelion-encoded harmony search heuristics for tree optimization problems

Abstract

Tree based optimization problems stand for those paradigms where solutions can be arranged within a tree-like graph whose nodes represent the optimization variables of the problem at hand and their interconnecting edges topological and/or hierarchical relationships between such variables. In this context, a research line of increasing interest during the last decade focuses on the derivation of intelligent solution encoding strategies capable of 1) capturing all topological constraints of this particular class of graphs; and 2) preserving their connectivity properties when they undergo combination/mutation operations within approximative evolutionary solvers. This manuscript takes a step over the state of the art by shedding light on the heritability properties of the Dandelion tree encoding approach under avant-garde stochastically-controlled evolutionary operators. In particular we elaborate on the topological heritability of the socalled Harmony Memory Considering Rate (HMCR) exploitative operator of the Harmony Search algorithm, a population-based meta-heuristic algorithm that has so far shown to outperform other evolutionary schemes in a wide range of optimization scenarios. Results from extensive Monte Carlo simulations are discussed in terms of the preserved structural properties of the newly produced solutions with respect to the initial Dandelionencoded population.

Joint Topology Optimization, Power Control and Spectrum Allocation for Intra-Vehicular Multi-hop Sensor Networks Using Dandelion-Encoded Heuristics

“…In fact, Dandelion codes have been shown to possess better locality and heritability characteristics than any other Cayley codes when undergoing different evolutionary operators [20,21]. This superior performance of Dandelion codes has unleashed a flurry of research gravitating on their application in different scenarios and sectors, such as Telecommunications [22] and Energy [23].…”