The paper "Abandoning Objectives: Evolution through the Search for Novelty Alone" by Joel Lehman and Kenneth O. Stanley explores the limitations of using objective functions in evolutionary computation (EC) and proposes a novel approach called *novelty search*. The authors argue that objective functions can be deceptive, leading search algorithms astray from the optimal path. They introduce novelty search, which focuses on finding novel behaviors rather than directly aiming at an objective. This approach is inspired by open-ended evolution in artificial life, where the goal is to continuously produce novel forms without a fixed objective. The paper discusses the challenges of deception in EC, such as local optima and the difficulty of crafting appropriate objective functions. It also reviews methods to mitigate deception, including diversity maintenance techniques and multi-objective optimization, but notes that these methods do not address the underlying pathology of deceptive objective functions. Novelty search is presented as a way to circumvent these issues by searching for behavioral novelty directly. The authors define a *novelty metric* that measures how unique an individual's behavior is, rewarding divergent and novel behaviors. This metric is integrated into the NEAT (NeuroEvolution of Augmenting Topologies) algorithm, which evolves artificial neural networks to control agents. Experiments in a two-dimensional maze navigation task and a three-dimensional biped locomotion domain demonstrate that novelty search outperforms objective-based search, even when the objective is to navigate the maze or walk a certain distance. This suggests that ignoring the objective can sometimes lead to better solutions, challenging the common belief that objectives are essential for effective search. The paper concludes by highlighting the inherent limitations of objective-based search and the potential benefits of novelty search, which can be applied to real-world problems and decouples open-ended search from artificial life worlds.The paper "Abandoning Objectives: Evolution through the Search for Novelty Alone" by Joel Lehman and Kenneth O. Stanley explores the limitations of using objective functions in evolutionary computation (EC) and proposes a novel approach called *novelty search*. The authors argue that objective functions can be deceptive, leading search algorithms astray from the optimal path. They introduce novelty search, which focuses on finding novel behaviors rather than directly aiming at an objective. This approach is inspired by open-ended evolution in artificial life, where the goal is to continuously produce novel forms without a fixed objective. The paper discusses the challenges of deception in EC, such as local optima and the difficulty of crafting appropriate objective functions. It also reviews methods to mitigate deception, including diversity maintenance techniques and multi-objective optimization, but notes that these methods do not address the underlying pathology of deceptive objective functions. Novelty search is presented as a way to circumvent these issues by searching for behavioral novelty directly. The authors define a *novelty metric* that measures how unique an individual's behavior is, rewarding divergent and novel behaviors. This metric is integrated into the NEAT (NeuroEvolution of Augmenting Topologies) algorithm, which evolves artificial neural networks to control agents. Experiments in a two-dimensional maze navigation task and a three-dimensional biped locomotion domain demonstrate that novelty search outperforms objective-based search, even when the objective is to navigate the maze or walk a certain distance. This suggests that ignoring the objective can sometimes lead to better solutions, challenging the common belief that objectives are essential for effective search. The paper concludes by highlighting the inherent limitations of objective-based search and the potential benefits of novelty search, which can be applied to real-world problems and decouples open-ended search from artificial life worlds.