In the realm of artificial intelligence (AI), Evolution Strategies (ES) form an integral part of the algorithmic components. These strategies represent an approach that is inspired by the principles of biological evolution such as natural selection, mutation, and crossover. The underlying focus is to construct machine learning models that can adapt, self-learn, and evolve, delivering smarter and more effective solutions in complex environments.

• Applied to the Population: ES is applied to a population of solutions rather than a single solution. As with natural evolution, each solution can be considered as an individual in a population pool. The fitness of an individual is evaluated, and the fittest individuals are replicated and mutated to create a new generation.
• High-Dimensional Continuous Optimization: ES is particularly suited to problems involving high-dimensional continuous optimization.
• Mutation and Recombination: Evolution Strategies use mutation and recombination to generate new potential solutions, focusing on the principle of survival of the fittest.
• Completely Black Box: ES can be applied to any problem as they require no knowledge about the structure of the underlying problem. In the context of AI and machine learning, this effectively eliminates the need for handcrafted gradients or feature selection.

Employing Evolution Strategies in AI necessitates a comprehensive understanding of the nature of problem space and the potential challenges inherent to the solution space. Once the problem domain has been thoroughly analyzed, a suitable ES-based model can be developed. However, while doing so, precautionary measures should be adopted to preempt potential issues that may arise from the drawbacks of ES.

Following the selection of an appropriate model, ES strategies can then be implemented following a systematic and organized pattern of mutation, cross-over, and selection to optimize solution space. This should be carried out in a carefully established framework that allows for a structured progression from an initial population towards generational improvements.

Performance of the ES-model should be closely monitored, ensuring the effectiveness of the solution. Evaluation criteria can be purpose-specific, based on the desired objectives of the task.

With this detailed understanding of Evolution Strategies in AI, it is apparent that they have significant potential in navigating complex problem spaces within AI and machine learning. This, when clubbed with a clear comprehension of their advantages and disadvantages, can result in the development and implementation of powerful AI and machine learning solutions. Indeed, the future of AI looks bright with the evolution of Evolution Strategies.

There are several distinct advantages associated with the application of Evolution Strategies in the context of artificial intelligence:

• Versatility: Evolution strategies, thanks to the black box nature, can be employed with versatile application. With no requirement for backpropagation, they can work where backpropagation fails, even without any gradient information.
• Efficiency: ES has shown definite efficiency when dealing with high-dimensional continuous optimization problems; these optimizations are relevant to numerous practical applications, including Neural Architecture Search (NAS).
• Simplicity: The absence of backpropagation allows it to favor simple solutions, reducing the complexity of the overall solution and increasing interpretability.
• Global Optimization: ES are particularly good at global optimization tasks in non-differentiable or non-convex problem spaces.
• Parallelism: ES inherently support parallelism; they can distribute the evaluation of candidate solutions across multiple cores, or even different machines, thereby enhancing the speed and efficiency of the exploration and optimization process.
• Robustness: They have demonstrated robustness to noise. This is particularly advantageous when optimizing stochastic policies.

Yet, with all advantages, there are some inherent drawbacks:

• Computational Intensity: ES require a significantly larger number of samples for learning. Therefore, they are more computationally intensive than the traditional gradient methods.
• Less Efficient on smooth problems: They are generally less efficient as compared to gradient-based methods when the problem is smooth and differentiable.
• Limited Adaptability: While they're good at adapting to the environment, certain issues arise when the environment changes too quickly.
• Greenfield Problems: Lack of problem-specific information can adversely impact the speed and precision of finding an optimal solution.
• High-dimensional Problems: Despite being suitable for high-dimensional problems, ES can take longer periods to compute the optimal solutions.