Evolutionary computation is a set of computer science methodologies inspired by the principles of natural evolution. Drawing upon scientific concepts such as reproduction, mutation, recombination, and natural selection, evolutionary computation devises artificial systems to solve complex problems for which standard problem-solving approaches may not be sufficient.

Universal Applicability: Evolutionary computation is a universal problem-solving approach adaptable for a broad array of applications. It can handle complex problems from diverse domains, including business, engineering, science, healthcare, and more.

Robustness: Evolutionary computation exhibits robustness, given that they tend to search for a global optimum and thus not easily get trapped in local optima. This quality makes them highly useful for problems with a complex solution landscape.

Scalability: The approaches of evolutionary computation can efficiently scale to problems with many variables or objectives. They can handle high-dimensional search problems, often better than classical optimization approaches.

Parallelism: Algorithms in evolutionary computation can take advantage of parallel and distributed computing, thus expediently finding solutions to time-consuming or otherwise complex problems.

Careful planning and a well-devised strategy are essential for implementing evolutionary computation methods effectively. This process starts with the precise definition of the problem, including describing the objectives and constraints. This problem definition is then used to design and configure an evolutionary algorithm. The algorithm undergoes testing and validation to ensure that it works properly. In case of failure, one needs to analyze the reasons and refine the algorithm accordingly. Successful application of evolutionary computation methods calls for a deep understanding of both the problem at hand and the principles of evolutionary algorithms.

It's worth noting that while evolutionary computation could find solutions to complex problems in a universal, robust, and scalable way, the efficacy of these methods may vary depending on the specific circumstances and demands of the problem. Visionary planning, practical assessment, and strategic adjustments to meet specific needs can foster the successful application of evolutionary computation. By carefully monitoring its implementation and performance, one can exploit the full potential of evolutionary computation.

Among the benefits that contribute to the growing prominence of evolutionary computation are:

• Problem Independence: Evolutionary computation techniques don't demand specific knowledge about the problem to be solved, they can be applied in a wide variety of contexts.
• Global Search: Due to the population-based search principle, the algorithms of evolutionary computation can avoid getting caught in local optima and hence, identify global solutions more consistently.
• Robustness: Algorithms based on evolutionary principles maintain a robust performance across a wide spectrum of problems.
• Diversity Preservation: The stochastic nature of evolutionary algorithms can preserve diversity within their population, ensuring a wide exploration of the search space.
• Optimization with multiple objectives: Evolutionary computation is adept at handling multi-objective optimization problems, a feature advantageous in real-world applications where several conflicting objectives frequently occur.

Despite the numerous advantages offered by evolutionary computation, it is crucial to bear in mind that no overarching problem-solving strategy is without its limitations.

Notwithstanding the broad benefits, the following downsides of using evolutionary computation must be acknowledged:

• Computational Expense: Since evolutionary computation methods primarily perform a global search, they might incur a higher computational cost than local search methods.
• Problem Definition: A problem has to be correctly formulated for an evolutionary algorithm, which can sometimes pose a challenge.
• Premature Convergence: The algorithms may sometimes converge prematurely to a sub-optimal solution, especially if the population lacks diversity.
• Lack of Predictability: Being stochastic methods, evolutionary computation methods are not always predictable in their performance.

In conclusion, the adoption of evolutionary computation as a global problem-solving method has been on the rise, attracting interest from various industry sectors for its versatility and robustness. It's vital to acknowledge and manage the potential limitations that may arise during the application to ensure successful results.