Evolutionary computation (EC) - a field that employs biologically inspired computational paradigms-is instrumental in the design of neural networks. This technique draws from natural selection's principles and applies them towards the optimization of learning systems. By emulating survival of the fittest, genetic mutations, and genetic crossover, evolutionary algorithms facilitate neural network design, expedite training algorithms, optimize hidden layers, and even aid in deciding activation functions.

Evolutionary computation is marked by its distinct attributes:

• Diverse Solutions: Evolutionary computation's inherent capability of developing a wide range of solutions, encompassing various possibilities, distinguishes it.
• Scalability: This technique effortlessly handles large-scale problems, fitting solutions from vast search spaces.
• Parallelism: It employs parallel search on multiple solutions, entrusting robustness and widened applicability.
• Natural Optimization: The application of natural evolution principles promotes an automatic optimization process.
• Flexibility: EC offers the flexibility of adopting any fitness function without needing gradient information.
• Adaptability: Due to its basis on abstract representations of problem-solving, EC can adapt to a broad range of problem types.

Evolutionary computation can be uniquely suited for neural network design as it allows mining of a diversified range of models, easing the identification of optimal network architecture, quicker convergence to solutions, and aiding in regularization.

Employing evolutionary computation for optimizing neural networks requires skills and knowledge in quatitative analysis, machine learning, and deep knowledge on neural networks. At the fundamental level, it involves defining the problem, selecting diploid or haploid genetic representation, outlining sets of genetic operators, and orchestrating these into evolutionary algorithm. In neural network framework, this process is further accented with the definition of network architecture, determination of weight values, and methodological training of the neural network.

Therefore, a successful implementation hinges on effective problem formulation, meticulous determination of network parameters, clear understanding of the evolutionary algorithms, and continual monitoring of the evolution process. Consequently, to ensure continuous improvement of solutions, a plenitude of testing and tuning is required commensurate with the ever evolving nature of neural network design and formulation options. A thorough understanding of the trade-offs related to the inherent dynamic tension between exploration of new solutions and exploiting known good solutions is also paramount.

Employing evolutionary computation for neural network design brings in several advantages:

• Time-Efficient: EC algorithms can process simultaneous evaluations of multiple solutions, thereby saving time invested in network training and design.
• Robustness: Evolutionary algorithms are notable for their robustness. Even when there's incomplete, noisy, or inconsistent data, these algorithms can still deliver satisfying results.
• Flexibility: The flexible nature of EC algorithms allows for easy incorporation into a variety of neural network designs - from topology to weight optimization.
• Automatic Feature Selection: It helps in automatic selection and engineering of features, thereby saving computational resources and improving network performance.

Despite the several advantages, a few challenges are associated with evolutionary computation:

• Computational Demand: Evolutionary algorithms can be computationally expensive, as they require several iterations of fitness evaluations.
• Premature Convergence: These algorithms may sometimes fall into premature convergence, where they may get stuck in sub-optimal solutions.
• Specific Problems: EC is not always the best fit. Some specific problem instances may best be served by other optimization methods.

In conclusion, despite a few associated challenges, evolutionary computation provides an exceptional paradigm for neural network design, offering solutions that are cost-effective, time-efficient, and flexible. It is hence recommended for organizations seeking to capitalize on artificial intelligence and robust optimization. Regular updates, advancements in computational capabilities and the increasing popularity of this technique in the tech-industry ensures that it will remain a valuable tool for neural network designers in the future.