Self-adjusting mutation rates let the (1+1) EA optimize the top k bits of BinVal in O(k^{1+ε}) time independent of n for all k in o(n) simultaneously.
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Energy-aware metaheuristics use an EI/J score to dynamically pick operators that maximize fitness gain per unit energy, reaching comparable fitness with substantially less energy than standard versions on knapsack, NK-landscapes, and error-correcting code problems.
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Anytime Analysis on BinVal: Adaptive Parameters Help
Self-adjusting mutation rates let the (1+1) EA optimize the top k bits of BinVal in O(k^{1+ε}) time independent of n for all k in o(n) simultaneously.
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Energy-Aware Metaheuristics
Energy-aware metaheuristics use an EI/J score to dynamically pick operators that maximize fitness gain per unit energy, reaching comparable fitness with substantially less energy than standard versions on knapsack, NK-landscapes, and error-correcting code problems.